Note: Descriptions are shown in the official language in which they were submitted.
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SYSTEMS AND METHODS FOR IMPROVING CONTENT RECOMMENDATIONS
USING A TRAINED MODEL
Background
[0001] The present disclosure is directed to systems and methods for providing
media
content recommendations, and in particular, for providing improved
recommendations
using a trained model.
Summary
[0002] Media content recommendations are used to improve the user experience
when
browsing for content. For example, media content recommendations have helped
users
find content in which the users may be interested in consuming. Media content
recommendation systems may provide content recommendations to a user which is
similar to previously consumed content by the user. Additionally or
alternatively,
recommendation systems may provide content recommendations to a user based on
content consumed by users who have similar interests as the user.
[0003] User preferences may change based on one or more factors corresponding
to
consuming content. For example, a user may prefer watching a relaxing movie
during
the evening of a weekday. The user may also prefer watching an action movie
during
the afternoon of a weekend. Conventional recommendation systems do not adapt
to
changing user preferences.
[0004] Content may have portions of interest to a user. For example, a user
may have
liked a particular scene in an episode of a series. The user may have watched
the
particular scene multiple times. The user may also skip other scenes in the
episode to
watch the particular scene. Conventional media recommendation systems may
provide
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content recommendations similar to the consumed content, which may still
include
scenes that the user prefers to skip. In such a case, the user has to filter
through the
recommended content for the portions of interest, which can be an arduous
process.
[0005] To address these shortcomings, systems and methods are described herein
for a
recommendations engine that generates content recommendations using a trained
model
that is personalized to information corresponding to content consumption.
[0006] It should be noted that a media guidance application may include or be
a
recommendations engine described herein. It should be noted that media
guidance data
described herein may include or be information corresponding to content
consumption
that a recommendations engine may use in the various techniques described in
the
present disclosure.
[0007] A trained model for generating content recommendations is provided. The
trained model may have been trained using a predefined set of training data.
The
predefined set of training data may be agnostic of profile data. For example,
the trained
model may have been trained without bias to the preferences of a particular
user.
[0008] The system may receive information corresponding to content
consumption.
The information corresponding to content consumption may also be referred to,
herein,
as content consumption data. In some embodiments, the information may include
information about user activity corresponding to content consumption (i.e.
user activity
data). In some embodiments, the information may include content metadata.
Content
metadata may indicate time, location, content type, and/or content genre of
consumed
content. In some embodiments, the information may include control activity
data.
Control activity data may indicate one or more control function selections
made by a
user while consuming content. For example, the activity data may indicate that
a user
enjoys listening to music associated with Game of Thrones during a weekend
night at
home. In some embodiments, the information may include information
corresponding
to portions of consumed content. For example, the activity data may include
skipped
scenes or repeated watching of a scene in Game of Thrones. In some
embodiments, the
information corresponding to content consumption may be associated with a
profile.
For example, a user may have watched and enjoyed an action scene in a series
such as
Game of Thrones. The corresponding activity indicating enjoyment of watching
the
action scene may be received by the system and saved in a profile.
[0009] An updated model may be generated based on the information
corresponding to
content consumption and on the provided model. The updated model may also be
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referred to, herein, as a personalized model, which is refined based on the
information
corresponding to content consumption. For example, a trained model may not
have
been trained indicating that a user likes Game of Thrones. A personalized
model may be
generated by updating the trained model with content consumption data from
when a
user watched Game of Thrones. In some embodiments, the personalized model may
be
a first updated model. In some embodiments, the personalized model may be
associated
with a profile. For example, the personalized model is linked to a profile and
the
associated activity.
[0010] The system may generate content recommendations using the personalized
model. For example, the personalized model may be used to output
recommendations
similar to Game of Thrones such as Lord of the Rings. The content
recommendations
may include previously consumed and unconsumed content. For example, the
recommendations may be provided at a time when a user may enjoy consuming the
content. For example, a user may have watched Game of Thrones over a year ago.
The
system, using the personalized model, may determine the user might enjoy
rewatching
Game of Thrones after a year based on the activity data. The personalized
model may
be used to generate Game of Thrones for recommendation based on that
determination.
[0011] The system may cause to be provided the content recommendations to a
user.
For example, the recommendation, Lord of the Rings, may be provided and
displayed on
a user device. In some embodiments, the content recommendations may be a first
set of
content recommendations.
[0012] In some embodiments, the system may receive additional information
corresponding to content consumption. In some embodiments, the additional
information may include activity corresponding to content recommendations
provided
by the system. Additionally or alternatively, the additional information may
include
activity corresponding to other consumed content recommendations. A second
updated
model may be generated based on the additional information and on the first
updated
model. In some embodiments, the second updated model is the personalized model
which is updated using the additional information corresponding to content
consumption. For example, a user may watch the recommendation, Lord of the
Rings,
and another content item such as House of Cards. The system receives related
activity
data and the personalized model may be updated to include the related activity
data. The
second updated model may be referred to as a second personalized model. The
personalized model may be updated any number of times and based on any
suitable
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trigger (e.g., periodically, by user request, when the system determines that
particular
previously unknown preferences of the user are significant enough, etc.). The
personalized model may be referred to, herein, as a first, second, third, etc.
updated
personalized model (or personalized model for the sake of brevity) based on
the number
of times the personalized model has been updated.
[0013] In some embodiments, the second updated model may be used to generate
additional content recommendations. For example, the system may use the second
personalized model to generate recommendations similar to any one or more
content
item consumed in the content consumption data. The additional content
recommendations may be a second set of content recommendations. The system may
cause to be provided the second content recommendations.
[0014] In some embodiments, generating the content recommendations may include
generating recommendations of portions of content items. For example, the
content
consumption data may have indicated that a user prefers action scenes in Game
of
Thrones. The personalized model may be used to generate, for example, scenes
similar
to the action scenes in Game of Thrones. In another example, the personalized
model
may be used to generate music similar to action scenes from Game of Thrones.
[0015] In some embodiments, the content recommendations may correspond to a
preferred content genre and/or a preferred subj ect. For example, content
consumption
data may indicate a user likes a specific actor such as Kit Harington and/or
thrilling
scenes such as a flight on the back of a dragon. The system may generate
content
recommendations which, for example, include Kit Harington and/or thrilling
flight
scenes.
[0016] In some embodiments, the information corresponding to content
consumption
may indicate preferred content genres. In such embodiments, the system may
rank the
content genres based on the content consumption data and generate a genre
ranking.
The personalized model may be generated based on the genre ranking. The system
may
use the updated model to generate content recommendations which are ordered
based on
the ranked themes.
[0017] In some embodiments, generating a personalized model based on the
trained
model and the content consumption data may include generating a model using a
recurrent neural network. For example, the recurrent neural network may be a
long
short-term memory recurrent neural network (LSTM RNN). Generating the model
using the recurrent neural network may include determining and updating state
data
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associated with a trained model and content consumption data. Generating the
model
using the recurrent neural network may include updating weights associated
with content
based on the model and the state data. The states associated with a model may
be
determined and updated iteratively based on the weights. The weights may be
optimized
based on the state data and the model. The personalized model may be generated
based
on the weights and the state data.
[0018] In some embodiments, the system may determine consumed portions of
content
associated with content consumption data. The activity data of the consumed
portions
may be indicative of a preferred length and/or content theme. The personalized
model
may be used to generate recommendations of portions corresponding to the
preferred
theme. The recommended portions may be modified to be the preferred length.
For
example, a user may have repeatedly watched an action scene from Game of
Thrones.
The action scene may have been seven minutes and the user may have skipped the
first
two minutes of the scene. Action scenes of content may be generated and
adjusted, for
example, to a length of five minutes. The system may provide the adjusted
scenes to a
device.
[0019] In some embodiments, the information corresponding to content
consumption
may include activity data associated with a social network. For example, a
user may
have indicated liking a specific episode of Game of Thrones on a social media
network
such as Twitter. The system may receive related activity data and generate an
updated
model that is based on the related activity data.
[0020] In some embodiments, a trained model may be provided that has been
updated
based on information about content consumption associated with a profile. The
information about content consumption may include information about
consumption of
portions of content items. A system may generate content recommendations using
the
trained model. In some embodiments, the system may generate content portion
recommendations based on content recommendations and on information about
consumption of portions of content items. The system may provide the content
portion
recommendations. For example, Lord of the Rings may be recommended using any
of
the models described in the present disclosure based on content consumption
data. A
system may generate and provide scene recommendations from Lord of the Rings
and/or
a similar series like Game of Thrones based on the content consumption data.
[0021] In some embodiments, a system may determine a portion of a content item
is
preferred based on information about content consumption. For example, a
system may
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determine a scene of Lord of the Rings is preferred if a user repeatedly
watches the
scene. A portion may be preferred based on a consumption preference associated
with a
profile. A consumption preference may include content genres and/or content
lengths.
A system may generate content portion recommendations based on the preferred
portion.
Additionally or alternatively, a system may determine a portion of a content
item is not
preferred based on information about content consumption. For example, a
system may
determine a scene of House of Cards is not preferred if a user skips the
scene. A system
may generate content portion recommendations based on the portion that is not
preferred. In some embodiments, the content recommendations and/or content
portion
modifications may be generated as modified portions of content items based on
a
preferred portion and/or a nonpreferred portion.
[0022] In some embodiments, a system may determine a preferred content genre
based
on the information about content consumption. The system may generate content
recommendations and/or content portion recommendations based on the preferred
genre.
Additionally or alternatively, a system may determine a preferred content item
length
based on the information about content consumption. The system may generate
content
recommendations and/or content portion recommendations based on the preferred
content item length. For example, a user may prefer to watch a final battle
scene in Lord
of the Rings. The user may have indicated preferring to watch an abridged
final battle
scene in Lord of the Rings without dialogue mixed into the battle scene by
skipping past
some or all of the dialogue. The system may generate a battle scene
recommendation
with little dialogue in Lord of the Rings or other recommended content. The
system
may determine a preferred scene length of five minutes from the final battle
scene based
on the skipping. The system may generate scene recommendations of around five
minutes.
[0023] It should be noted, the systems and/or methods described above may be
applied
to, or used in accordance with, other systems, methods and/or apparatuses.
Brief Description of the Drawings
[0024] The above and other objects and advantages of the disclosure will be
apparent
upon consideration of the following detailed description, taken in conjunction
with the
accompanying drawings, in which like reference characters refer to like parts
throughout, and in which:
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[0025] FIG. 1 shows an illustrative flow diagram in a system providing content
recommendations and improving recommendations based on content consumption
data,
in accordance with some embodiments of the disclosure;
[0026] FIG. 2 shows an illustrative block diagram of a system using a trained
model to
generate content recommendations and repeatedly updating the model based on
activity
data, in accordance with some embodiments of the disclosure;
[0027] FIG. 3 shows an illustrative block diagram of a system providing
recommendations and updating a personalized model based on activity data for
the
provided recommendations, in accordance with some embodiments of the
disclosure;
[0028] FIG. 4 shows an illustrative block diagram of a system using a model
along
with various types of activity data to generate various content
recommendations, in
accordance with some embodiments of the disclosure;
[0029] FIG. 5 shows an illustrative block diagram of a system including a
recommendations engine and communications network, in accordance with some
embodiments of the disclosure;
[0030] FIG. 6 shows an illustrative block diagram of a user equipment device,
in
accordance with some embodiments of the disclosure;
[0031] FIG. 7 shows an illustrative block diagram of a media system, in
accordance
with some embodiments of the disclosure;
[0032] FIG. 8 shows a flowchart of a process for providing content
recommendations
using a personalized model, in accordance with some embodiments of the
disclosure;
[0033] FIG. 9 shows a flowchart of a process for providing content
recommendations
using an updated personalized model, in accordance with some embodiments of
the
disclosure;
[0034] FIG. 10 shows a flowchart of a process for generating a personalized
model
including a genre ranking, in accordance with some embodiments of the
disclosure;
[0035] FIG. 11 shows a flowchart of a process for generating a personalized
model
based on state data and optimized weights, in accordance with some embodiments
of the
disclosure;
[0036] FIG. 12 shows an illustrative block diagram of a system for providing
content
portion recommendations using a trained model, in accordance with some
embodiments
of the disclosure;
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[0037] FIG. 13 shows a flowchart of a process for providing content portion
recommendations based on content recommendations, in accordance with some
embodiments of the disclosure;
[0038] FIG. 14 shows a flowchart of a process for generating content portion
recommendations based on one or more preferred and/or nonpreferred portions of
a
content item, in accordance with some embodiments of the disclosure.
Detailed Description
[0039] Systems and methods are described herein for a recommendations engine
that
generates content recommendations using a trained model that is personalized
to
information corresponding to content consumption.
[0040] It should be noted that a media guidance application may include or be
a
recommendations engine described herein. It should be noted that media
guidance data
described herein may include or be information corresponding to content
consumption
that a recommendations engine may use in the various techniques described in
the
present disclosure.
[0041] As referred to herein, the term "content" should be understood to mean
an
electronically consumable asset accessed using any suitable electronic
platform, such as
broadcast television programming, pay-per-view programs, on-demand programs
(as in
video-on-demand (VOD) systems), Internet content (e.g., streaming content,
downloadable content, Webcasts, etc.), video clips, audio, information about
content,
images, animations, documents, playlists, websites and webpages, articles,
books,
electronic books, blogs, chat sessions, social media, software applications,
games,
virtual reality media, augmented reality media, and/or any other media or
multimedia
and/or any combination thereof
[0042] FIG. 1 shows an illustrative flow diagram in a system providing content
recommendations and improving recommendations based on content consumption
data,
in accordance with some embodiments of the disclosure. System 100 includes
recommendations engine 102. Recommendations engine 102 may provide content
recommendations 104 to user equipment 106. Information corresponding to
consumed
content (i.e. content consumption data 108) may be collected and provided to
recommendations engine 102, which may generate additional content
recommendations
based on content consumption data 108. System 100 may perform the described
steps in
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a loop to improve provided content recommendations 104 using new content
consumption data 108. System 100 may improve content recommendations 104 and
filter points of interest based on content consumption data 108 by using a
trained model
(e.g. a long short-term memory recurrent neural network (LSTM RNN) model).
[0043] In some embodiments, the trained model may be a long short-term memory
recurrent neural network model. The advantage of LSTM RNN over other neural
networks in regards to the present disclosure is that a LSTM RNN can learn
from
content consumption data which was not available when first training a model
for
providing content recommendations. The LSTM RNN provides the advantage of
including past and new content consumption data in a LSTM RNN model while
concurrently using the LSTM RNN model to provide content recommendations.
System
100 saves all the content consumption data corresponding to content
consumption. The
content consumption data may include fully watched content or portions of
content. The
portions of content may be indicative of points of interest. The LSTM RNN may
be
trained in a supervised fashion on a set of training sequences (i.e. training
data) by using
machine learning techniques (e.g. gradient descent and backpropagation through
the
content consumption data). The described optimization may compute gradients to
change one or more weights of the LSTM RNN model. Unnecessary or wrong
recommendations, which indicate errors in the weights, may be looped in a
feedback
loop using machine learning techniques (e.g. gradient descent and
backpropagation)
through the LSTM RNN to generate one or more optimized weights.
[0044] Content recommendations and portions of content recommendations may be
generated using recommendations engine 102. Again, this output will be back
propagated in the LSTM RNN network to further improve recommendations and
better
recommendation of points of interest in the content. For example, a user may
watch all
seasons of a series, such as Game of Thrones. Game of Thrones includes
multiple
content themes and genres such as witty humor, fights, suspense, thriller,
romance, pity,
revenge, surprise, etc. For example, an episode in the series might be watched
fully or
partially. The episode may include scenes of particular interest to a user and
may be
watched multiple times. Some episodes may be watched only once fully or only
partially. For example, a user may have enjoyed playing action games. For
example, a
user may enjoy reading e-books. Content consumption data 108 may include such
example activities and may be used to train and/or update a model (e.g. a LSTM
RNN
model). The model may include data about the scenes of interest from an
episode based
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on content consumption data. Additionally or alternatively, the model may
include data
about scenes of disinterest based on content consumption data. System 100 may
generate new content recommendations based on preferences using the model. In
some
embodiments, portions (e.g. scenes) of interest may be recommended which may
be
filtered content from previously consumed content. That filtered content may
be sorted
and/or ranked based on a variety of criteria (e.g. by preferred content themes
and/or
genres based on content consumption data 108) and may be recommended. New
content
consumption data (e.g. content consumption data 108) based on content
recommendations 104 may be provided to the LSTM RNN model, which may improve
.. future content recommendations 104.
[0045] Portions of content recommendations may be consumed. In a non-limiting
example, a scene from recommended content is of length seven minutes. A user
may
watch the first five mins and then skip the last two mins. Content consumption
data 108
may include that information and the LSTM RNN model may be updated based on
the
information. In another non-limiting example, a user may watch provided
content
recommendations 104 fully or may only watch portions of interest. Content
consumption data 108 may also include that information and the LSTM RNN model
may be further updated based on the information. The LSTM RNN model is further
described in relation to FIG. 11.
[0046] FIG. 2 shows an illustrative block diagram of a system 200 using a
model to
generate content recommendations and continually update the model based on new
content consumption data to generate improved content recommendations, in
accordance
with some embodiments of the disclosure. System 200 includes recommendations
engine 202. System 200 may receive training data 204 via recommendations
engine
202. In some embodiments, training data 204 may be a predefined set of
training data
that is agnostic of a particular profile. Recommendations engine 202 may
provide a
trained model 206 based on training data 204. Trained model 206 may be
combined
with information corresponding to content consumption (i.e. content
consumption data
208) to generate an updated or personalized model 210. Content consumption
data 208
may include activity data corresponding to content consumption of a specific
profile.
Personalized model 210 may be personalized to a specific profile based on
content
consumption data 208. Personalized model 210 may be generated by
recommendations
engine 202. For example, a trained model may not have been trained indicating
that a
user likes Game of Thrones. A personalized model may be generated by updating
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trained model with content consumption data from when a user watched Game of
Thrones. In some embodiments, the personalized model may be a first updated
model.
In some embodiments, the personalized model may be associated with a profile.
For
example, the personalized model is linked to a user profile and the associated
activity.
For example, the personalized model is linked to a gaming profile. For
example, the
personalized model is linked to a reading profile.
[0047] In some embodiments, the information may include content metadata.
Content
metadata may indicate time, location, content type, and/or content genre of
consumed
content. The information may also include control activity data. Control
activity data
may indicate one or more control function selections made while consuming
content.
For example, the activity data may indicate that a user enjoys listening to
music
associated with Game of Thrones during a weekend night at home. In some
embodiments, the information may include information corresponding to portions
of
consumed content. For example, the activity data may include skipped scenes or
repeated watching of a scene in Game of Thrones. For example, the activity
data may
include finishing the same game multiple times. For example, the activity data
may
include activity related to virtual reality content. In some embodiments, the
information
corresponding to content consumption may be associated with a profile. For
example, a
user may have watched and enjoyed an action scene in a series such as Game of
Thrones. The corresponding activity indicating enjoyment of watching the
action scene
may be received by the system and saved in the user's profile.
[0048] Personalized model 210 may be used to generate content recommendations
212. Content recommendations 212 may be provided to user equipment 214 using,
as
non-limiting examples, input/output circuitry, communications circuitry,
and/or a
communications network. New content consumption data 216 corresponding to
content
consumption may be collected and received by system 200. Content consumption
data
216 may include activity data corresponding to consumption of content
recommendations 212. Content consumption data 216 may include activity data
corresponding to consumption of other content. Personalized model 210 may be
updated using new activity data 216. The updated personalized model 210 may be
used
to generate more content recommendations 212. System 200 may update
personalized
model 210 using new content consumption data 216 and generate more content
recommendations 212 in a continuous loop 218.
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[0049] Content recommendations 212 may include previously consumed and
unconsumed content. Recommendations 212 may be provided at a time when
consumption data 208 may be indicative of enjoyment of consuming the content.
System 200 may determine a period of time after which consumption of
previously
consumed content may preferred. For example, a user may have watched Game of
Thrones over a year ago. System 200, based on content consumption data 208 or
new
content consumption data 216, may determine the user might enjoy rewatching
Game of
Thrones after a year based on the activity data. Personalized model 210 may be
used to
generate Game of Thrones for recommendation in response to the determining.
The
system may cause to be provided the content recommendations. For example, a
content
recommendation, such as Lord of the Rings, may be provided and displayed on a
device.
In some embodiments, the content recommendations may be a first set of content
recommendations (e.g. as part of loop 218). For example, personalized model
210 may
be used to generate a game related to Game of Thrones. For example,
personalized
model 210 may be used to generate VR content related to Game of Thrones.
[0050] FIG. 3 shows an illustrative block diagram of a system 300 providing
recommendations and updating a personalized model based on activity data for
the
provided recommendations, in accordance with some embodiments of the
disclosure.
System 300 may include personalized model 302. For example, personalized model
302
can be personalized model 210. Personalized model 302 may be used to provide a
first
set of content recommendations 304 to user equipment 306. System 300 may
receive
additional information corresponding to content consumption. For example, new
content consumption data 308 may be collected and transmitted. In some
embodiments,
content consumption data 308 may be activity data corresponding to consumption
of
recommendations 304. In some embodiments, the additional information may
include
activity corresponding to content recommendations provided by the system.
Additionally or alternatively, the additional information may include activity
corresponding to other content recommendations consumed by a user.
Personalized
model 302 and activity data 308 may be used to generate an updated
personalized model
308. Updated personalized model 310 may be a second updated model. For
example,
recommendations engine 202 may generate updated model 310. Updated model 308
may be used to generate a second set of content recommendations 310. In some
embodiments, system 300 may be considered a single occurrence of loop 218
containing
model 210, recommendations 212, device 214, and content consumption data 216.
For
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example, updated personalized model 310 can be updated personalized model 210
after
loop 218. For example, a user may watch the recommendation, Lord of the Rings,
and
another content item such as House of Cards. The system receives related
activity data
and the personalized model may be updated to include the related activity
data. The
second updated model may be referred to as a second personalized model.
[0051] In some embodiments, the second updated model may be used to generate
additional content recommendations. For example, the system may use the second
personalized model to generate recommendations similar to any content item in
the
content consumption data. The additional content recommendations may be a
second set
of content recommendations. The system may cause to be provided the second
content
recommendations.
[0052] In some embodiments, generating the content recommendations may include
generating recommendations of portions of content items. For example, the
content
consumption data may have indicated that the user prefers action scenes in
Game of
Thrones. The personalized model may be used to generate, for example, scenes
similar
to the action scenes in Game of Thrones. In another example, the personalized
model
may be used to generate music similar to action scenes from Game of Thrones.
[0053] FIG. 4 shows an illustrative block diagram of a system 400 using a
model
along with various types of activity data to generate various content
recommendations,
in accordance with some embodiments of the disclosure. System 400 may include
personalized model 402. Content consumption data 404 shows exemplary
categories of
activity data that may be used with personalized model 402 to generate content
recommendations. Content consumption data 404 may include content metadata 406
and control activity data 408. Content consumption data may also include data
indicating fully consumed content 410, partially consumed content 412, or
consumption
frequency of content 414. Content consumption data may also include activity
data 416
indicating activity on a social network. It should be noted that data 406-416
are shown
separately for clarity and that content consumption data 404 may include any
one, any
combination, or all of data 406-416. Content consumption data 404 may be a mix
of any
of data 406-416 and appropriate techniques to organize content consumption
data 404
may be involved as part of the systems and methods described in the present
disclosure.
System 400 may generate content recommendations using personalized model 402
and
content consumption data 404. The content recommendations may include
recommended portions of content 418. The content recommendations may include
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ranked content recommendations 420 based on a generated genre ranking. The
content
recommendations may include modified content 422. Modified content 422 may be
modified based on the content consumption data. For example, out of a seven
minute
length scene from an episode of Game of Thrones, a user may only watch
starting from
the third minute to the seventh minute of the scene. The system may determine
that the
user prefers to watch the four minute length part of the scene. When
generating the
recommended content, the system may modify the length of the content or
recommended portions of the content based on the four minute part of the
scene. It
should be noted that recommendations data 418-422 are shown separately for
clarity and
may be included in or be a single entity (e.g. content recommendations 212).
The
system may cause to provide the generated content recommendations to exemplary
user
equipment 424.
[0054] FIG. 5 shows an illustrative block diagram of a system 500 to generate
and
provide content recommendations, in accordance to some embodiments of the
disclosure. System 500 may include recommendations engine 502 and
communications
network 512. In some embodiments, the recommendations engine may be
communicatively connected to a user interface. Recommendations engine 502 may
include control circuitry 504, processing circuitry 506, communications
circuitry 508,
and storage 510 (e.g., RAM, ROM, hard disk, removable disk, etc.) with
corresponding
storage circuitry. Control circuitry 504 may be based on any suitable
processing
circuitry such as processing circuitry 506. As referred to herein, processing
circuitry
should be understood to mean circuitry based on one or more microprocessors,
microcontrollers, digital signal processors, programmable logic devices, field-
programmable gate arrays (FPGAs), application-specific integrated circuits
(ASICs),
.. etc., and may include a multi-core processor (e.g., dual-core, quad-core,
hexa-core, or
any suitable number of cores) or supercomputer. In some embodiments,
processing
circuitry may be distributed across multiple separate processors or processing
units, for
example, multiple of the same type of processing units (e.g. two Intel Core i7
processors) or multiple different processors (e.g., an Intel Core i5 processor
and an Intel
Core i7 processor). In some embodiments, control circuitry 504 executes
instructions
for a recommendations engine stored in memory (e.g., storage 510). In some
embodiments, recommendations engine 502 may be (or included in) a cloud
entertainment service system (e.g. a cloud gaming console). A cloud
entertainment
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service system may substantially perform any or all of the functions of user
equipment
and provide content to user equipment in a format suitable for consumption.
[0055] Memory may be an electronic storage device provided as storage 510 that
is
part of control circuitry 504. As referred to herein, the phrase "electronic
storage
device" or "storage device" should be understood to mean any device for
storing
electronic data, computer software, or firmware, such as random-access memory,
read-
only memory, hard drives, optical drives, digital video disc (DVD) recorders,
BLU-
RAY disc (BD) recorders, BLU-RAY 3D disc recorders, digital video recorders
(DVR,
sometimes called a personal video recorder, or PVR), solid state devices,
quantum
storage devices, gaming consoles, gaming media, or any other suitable fixed or
removable storage devices, and/or any combination of the same. Storage 510 may
be
used to store various types of content described herein as well as activity
data described
above. Nonvolatile memory may also be used (e.g., to launch a boot-up routine
and
other instructions). Cloud-based storage, described in relation to FIG. 7, may
be used to
supplement storage 510 or instead of storage 510.
[0056] Recommendations engine 502 may be part of server-side equipment,
connected
to client-side equipment using communications network 512 as described herein.
In
client-server based embodiments, control circuitry 504 may include
communications
circuitry 508 suitable for communicating with a guidance application server or
other
networks or client devices. The instructions for carrying out the above-
mentioned
functionality may be stored on the guidance application server. Communications
circuitry 508 may include a cable modem, an integrated services digital
network (ISDN)
modem, a digital subscriber line (DSL) modem, Ethernet card, or a wireless
modem for
communications with other equipment, or any other suitable communications
circuitry.
Such communications may involve the Internet or any other suitable
communications
networks or paths (which is described in more detail in connection with FIG.
7). In
addition, communications circuitry may include circuitry that enables peer-to-
peer
communication of user equipment devices, or communication of user equipment
devices
in locations remote from each other.
[0057] Recommendations engine 502 may be connected to a communications network
512. Communications network 512 may also be connected to, for example,
servers,
devices, and other user equipment. Communications network 512 may be one or
more
networks including the Internet, a mobile phone network, mobile voice or data
network
(e.g., a 5G, 4G or LTE network), mesh network, peer-to-peer network, cable
network,
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cable reception (e.g., coaxial), microwave link, DSL reception, cable internet
reception,
fiber reception, over-the-air infrastructure, optical wireless communications
(e.g. Li-Fi),
or other types of communications network or combinations of communications
networks. The recommendations engine may be coupled to a secondary
communication
network (e.g., Bluetooth, Bluetooth Low Energy, Near Field Communication,
service
provider proprietary networks, or wired connection) to the selected device for
generation
for playback. Paths may separately or together include one or more
communications
paths, such as a satellite path, a fiber-optic path, a cable path, a path that
supports
Internet communications, free-space connections (e.g., for broadcast or other
wireless
signals), or any other suitable wired or wireless communications path or
combination of
such paths.
[0058] Recommendations engine 502 may receive and transmit content and data
via
input/output (I/0) path 514. I/0 path 514 may provide content (e.g., broadcast
programming, on-demand programming, Internet content, content available over a
local
.. area network (LAN) or wide area network (WAN), and/or other content) and
data to
recommendations engine 502. Control circuitry 504 may be used or cause to send
and
receive commands, requests, and other suitable data using I/0 path 514. I/0
path 514
may connect any of control circuitry 504, processing circuitry 506,
communications
circuitry 508, and storage 510 to one or more communications paths. I/0
functions may
be provided by one or more of these communications paths but are shown as a
single
path in FIG. 5 to avoid overcomplicating the drawing.
[0059] FIG. 6 is an illustrative block diagram showing exemplary user
equipment 600,
in accordance with some embodiments of the disclosure. User equipment device
600
may receive content and data via input/output (hereinafter "I/0") path 602.
I/0 path 602
may provide content (e.g., broadcast programming, on-demand programming,
Internet
content, content available over a local area network (LAN) or wide area
network
(WAN), and/or other content) and data to control circuitry 604, which includes
processing circuitry 606 and storage 608. Control circuitry 604 may be used to
send and
receive commands, requests, and other suitable data using I/0 path 602. I/0
path 602
may connect control circuitry 604 (and specifically processing circuitry 606)
to one or
more communications paths (described below). I/0 functions may be provided by
one
or more of these communications paths, but are shown as a single path in FIG.
6 to
avoid overcomplicating the drawing. In some embodiments, a cloud entertainment
service system (e.g. a cloud gaming console) may substantially perform any or
all of the
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functions of user equipment described herein and provide content to remote
user
equipment in a format suitable for consumption.
[0060] Control circuitry 604 may be based on any suitable processing circuitry
such as
processing circuitry 606. As referred to herein, processing circuitry should
be
understood to mean circuitry based on one or more microprocessors,
microcontrollers,
digital signal processors, programmable logic devices, field-programmable gate
arrays
(FPGAs), application-specific integrated circuits (ASICs), etc., and may
include a multi-
core processor (e.g., dual-core, quad-core, hexa-core, or any suitable number
of cores) or
supercomputer. In some embodiments, processing circuitry may be distributed
across
multiple separate processors or processing units, for example, multiple of the
same type
of processing units (e.g., two Intel Core i7 processors) or multiple different
processors
(e.g., an Intel Core i5 processor and an Intel Core i7 processor). In some
embodiments,
control circuitry 604 executes instructions for a media guidance application
stored in
memory (i.e., storage 608). A media guidance application may include or be a
recommendations engine. Specifically, control circuitry 604 may be instructed
by the
media guidance application to perform the functions discussed above and below.
For
example, the media guidance application may provide instructions to control
circuitry
604 to generate the media guidance displays. In some implementations, any
action
performed by control circuitry 604 may be based on instructions received from
the
media guidance application.
[0061] In client-server based embodiments, control circuitry 604 may include
communications circuitry suitable for communicating with a guidance
application server
or other networks or servers. The instructions for carrying out the above
mentioned
functionality may be stored on the guidance application server. Communications
circuitry may include a cable modem, an integrated services digital network
(ISDN)
modem, a digital subscriber line (DSL) modem, Ethernet card, or a wireless
modem for
communications with other equipment, or any other suitable communications
circuitry.
Such communications may involve the Internet or any other suitable
communications
networks or paths (which is described in more detail in connection with FIG.
7). In
addition, communications circuitry may include circuitry that enables peer-to-
peer
communication of user equipment devices, or communication of user equipment
devices
in locations remote from each other.
[0062] Memory may be an electronic storage device provided as storage 608 that
is
part of control circuitry 604. As referred to herein, the phrase "electronic
storage
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device" or "storage device" should be understood to mean any device for
storing
electronic data, computer software, or firmware, such as random-access memory,
read-
only memory, hard drives, optical drives, digital video disc (DVD) recorders,
BLU-
RAY disc (BD) recorders, BLU-RAY 3D disc recorders, digital video recorders
(DVR,
sometimes called a personal video recorder, or PVR), solid state devices,
quantum
storage devices, gaming consoles, gaming media, or any other suitable fixed or
removable storage devices, and/or any combination of the same. Storage 608 may
be
used to store various types of content described herein as well as media
guidance data
described above. Media guidance data may include or be content consumption
data.
Nonvolatile memory may also be used (e.g., to launch a boot-up routine and
other
instructions). Cloud-based storage, described in relation to FIG. 7, may be
used to
supplement storage 608 or instead of storage 608.
[0063] Control circuitry 604 may include video generating circuitry and tuning
circuitry, such as one or more analog tuners, one or more 1VIPEG-2 decoders or
other
digital decoding circuitry, high-definition tuners, or any other suitable
tuning or video
circuits or combinations of such circuits. Encoding circuitry (e.g., for
converting over-
the-air, analog, or digital signals to MPEG signals for storage) may also be
provided.
Control circuitry 604 may also include scaler circuitry for upconverting and
downconverting content into the preferred output format of the user equipment
600.
Circuitry 604 may also include digital-to-analog converter circuitry and
analog-to-digital
converter circuitry for converting between digital and analog signals. The
tuning and
encoding circuitry may be used by the user equipment device to receive and to
display,
to play, or to record content. The tuning and encoding circuitry may also be
used to
receive guidance data. The circuitry described herein, including for example,
the tuning,
video generating, encoding, decoding, encrypting, decrypting, scaler, and
analog/digital
circuitry, may be implemented using software running on one or more general
purpose
or specialized processors. Multiple tuners may be provided to handle
simultaneous
tuning functions (e.g., watch and record functions, picture-in-picture (PIP)
functions,
multiple-tuner recording, etc.). If storage 608 is provided as a separate
device from user
equipment 600, the tuning and encoding circuitry (including multiple tuners)
may be
associated with storage 608.
[0064] A user may send instructions to control circuitry 604 using user input
interface
610. User input interface 610 may be any suitable user interface, such as a
remote
control, mouse, trackball, keypad, keyboard, touch screen, touchpad, stylus
input,
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joystick, voice recognition interface, or other user input interfaces. Display
612 may be
provided as a stand-alone device or integrated with other elements of user
equipment
device 600. For example, display 612 may be a touchscreen or touch-sensitive
display.
In such circumstances, user input interface 610 may be integrated with or
combined with
display 612. Display 612 may be one or more of a monitor, a television, a
liquid crystal
display (LCD) for a mobile device, amorphous silicon display, low temperature
poly
silicon display, electronic ink display, electrophoretic display, active
matrix display,
electro-wetting display, electrofluidic display, light-emitting diode display,
electroluminescent display, plasma display panel, high-performance addressing
display,
thin-film transistor display, organic light-emitting diode display, surface-
conduction
electron-emitter display (SED), laser television, carbon nanotubes, quantum
dot display,
interferometric modulator display, or any other suitable equipment for
displaying visual
images. In some embodiments, display 612 may be HDTV-capable. In some
embodiments, display 612 may be a 3D display, and the interactive media
guidance
.. application and any suitable content may be displayed in 3D. A video card
or graphics
card may generate the output to the display 612. The video card may offer
various
functions such as accelerated rendering of 3D scenes and 2D graphics, MPEG-
2/MPEG-
4 decoding, TV output, or the ability to connect multiple monitors. The video
card may
be any processing circuitry described above in relation to control circuitry
604. The
video card may be integrated with the control circuitry 604. Speakers 614 may
be
provided as integrated with other elements of user equipment device 600 or may
be
stand-alone units. The audio component of videos and other content displayed
on
display 612 may be played through speakers 614. In some embodiments, the audio
may
be distributed to a receiver (not shown), which processes and outputs the
audio via
speakers 614.
[0065] The guidance application may be implemented using any suitable
architecture.
For example, it may be a stand-alone application wholly-implemented on user
equipment device 600. In such an approach, instructions of the application are
stored
locally (e.g., in storage 608), and data for use by the application is
downloaded on a
periodic basis (e.g., from an out-of-band feed, from an Internet resource, or
using
another suitable approach). Control circuitry 604 may retrieve instructions of
the
application from storage 608 and process the instructions to generate any of
the displays
discussed herein. Based on the processed instructions, control circuitry 604
may
determine what action to perform when input is received from input interface
610. For
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example, movement of a cursor on a display up/down may be indicated by the
processed
instructions when input interface 610 indicates that an up/down button was
selected.
[0066] In some embodiments, the media guidance application is a client-server
based
application. Data for use by a thick or thin client implemented on user
equipment device
600 is retrieved on-demand by issuing requests to a server remote to the user
equipment
device 600. In one example of a client-server based guidance application,
control
circuitry 604 runs a web browser that interprets web pages provided by a
remote server.
For example, the remote server may store the instructions for the application
in a storage
device. The remote server may process the stored instructions using circuitry
(e.g.,
control circuitry 604) and generate the displays discussed above and below.
The client
device may receive the displays generated by the remote server and may display
the
content of the displays locally on equipment device 600. This way, the
processing of the
instructions is performed remotely by the server while the resulting displays
are
provided locally on equipment device 600. Equipment device 600 may receive
inputs
.. from the user via input interface 610 and transmit those inputs to the
remote server for
processing and generating the corresponding displays. For example, equipment
device
600 may transmit a communication to the remote server indicating that an
up/down
button was selected via input interface 610. The remote server may process
instructions
in accordance with that input and generate a display of the application
corresponding to
the input (e.g., a display that moves a cursor up/down). The generated display
is then
transmitted to equipment device 600 for presentation to the user.
[0067] In some embodiments, the media guidance application is downloaded and
interpreted or otherwise run by an interpreter or virtual machine (run by
control circuitry
604). In some embodiments, the guidance application may be encoded in the ETV
.. Binary Interchange Format (EBIF), received by control circuitry 604 as part
of a suitable
feed, and interpreted by a user agent running on control circuitry 604. For
example, the
guidance application may be an EBIF application. In some embodiments, the
guidance
application may be defined by a series of JAVA-based files that are received
and run by
a local virtual machine or other suitable middleware executed by control
circuitry 604.
In some of such embodiments (e.g., those employing MPEG-2 or other digital
media
encoding schemes), the guidance application may be, for example, encoded and
transmitted in an MPEG-2 object carousel with the MPEG audio and video packets
of a
program.
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[0068] User equipment device 600 of FIG. 6 can be implemented in system 700 of
FIG. 7 as user television equipment 702, user computer equipment 704, wireless
user
communications device 706, or any other type of user equipment suitable for
accessing
content, such as a non-portable gaming machine. For simplicity, these devices
may be
referred to herein collectively as user equipment or user equipment devices
and may be
substantially similar to user equipment devices described above. User
equipment
devices, on which a media guidance application may be implemented, may
function as a
standalone device or may be part of a network of devices. Various network
configurations of devices may be implemented and are discussed in more detail
below.
[0069] A user equipment device utilizing at least some of the system features
described above in connection with FIG. 6 may not be classified solely as user
television
equipment 702, user computer equipment 704, or a wireless user communications
device
706. For example, user television equipment 702 may, like some user computer
equipment 704, be Internet-enabled allowing for access to Internet content,
while user
__ computer equipment 704 may, like some television equipment 702, include a
tuner
allowing for access to television programming. The media guidance application
may
have the same layout on various different types of user equipment or may be
tailored to
the display capabilities of the user equipment. For example, on user computer
equipment 704, the guidance application may be provided as a web site accessed
by a
web browser. In another example, the guidance application may be scaled down
for
wireless user communications devices 706.
[0070] In system 700, there is typically more than one of each type of user
equipment
device but only one of each is shown in FIG. 7 to avoid overcomplicating the
drawing.
In addition, each user may utilize more than one type of user equipment device
and also
__ more than one of each type of user equipment device. For example, user
equipment may
include an infotainment console in a vehicle. For example, user equipment may
include
a virtual reality (VR) device, an augmented reality (AR) device, a mobile
phone, and/or
a cloud gaming console.
[0071] In some embodiments, a user equipment device (e.g., user television
equipment
702, user computer equipment 704, wireless user communications device 706) may
be
referred to as a "second screen device." For example, a second screen device
may
supplement content presented on a first user equipment device. The content
presented
on the second screen device may be any suitable content that supplements the
content
presented on the first device. In some embodiments, the second screen device
provides
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an interface for adjusting settings and display preferences of the first
device. In some
embodiments, the second screen device is configured for interacting with other
second
screen devices or for interacting with a social network. The second screen
device can be
located in the same room as the first device, a different room from the first
device but in
the same house or building, or in a different building from the first device.
[0072] The user may also set various settings to maintain consistent media
guidance
application settings across in-home devices and remote devices. Settings
include those
described herein, as well as channel and program favorites, programming
preferences
that the guidance application utilizes to make programming recommendations,
display
preferences, and other desirable guidance settings. For example, if a user
sets a channel
as a favorite on, for example, the web site www.allrovi.com on their personal
computer
at their office, the same channel would appear as a favorite on the user's in-
home devices
(e.g., user television equipment and user computer equipment) as well as the
user's
mobile devices, if desired. Therefore, changes made on one user equipment
device can
change the guidance experience on another user equipment device, regardless of
whether
they are the same or a different type of user equipment device. In addition,
the changes
made may be based on settings input by a user, as well as user activity
monitored by the
guidance application.
[0073] The user equipment devices may be coupled to communications network
714.
Namely, user television equipment 702, user computer equipment 704, and
wireless user
communications device 706 are coupled to communications network 714 via
communications paths 708, 710, and 712, respectively. Communications network
714
may be one or more networks including the Internet, a mobile phone network,
mobile
voice or data network (e.g., a 4G or LTE network), cable network, public
switched
telephone network, optical wireless communications network (e.g. Li-Fi), or
other types
of communications network or combinations of communications networks. Paths
708,
710, and 712 may separately or together include one or more communications
paths,
such as, a satellite path, a fiber-optic path, a cable path, a path that
supports Internet
communications (e.g., IPTV), free-space connections (e.g., for broadcast or
other
wireless signals), or any other suitable wired or wireless communications path
or
combination of such paths. Path 712 is drawn with dotted lines to indicate
that in the
exemplary embodiment shown in FIG. 7 it is a wireless path and paths 708 and
710 are
drawn as solid lines to indicate they are wired paths (although these paths
may be
wireless paths, if desired). Communications with the user equipment devices
may be
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provided by one or more of these communications paths but are shown as a
single path
in FIG. 7 to avoid overcomplicating the drawing.
[0074] Although communications paths are not drawn between user equipment
devices, these devices may communicate directly with each other via
communication
paths, such as those described above in connection with paths 708, 710, and
712, as well
as other short-range point-to-point communication paths, such as USB cables,
IEEE
1394 cables, wireless paths (e.g., Bluetooth, Bluetooth Low Energy, infrared,
IEEE 802-
11x, etc.), or other short-range communication via wired or wireless paths.
BLUETOOTH is a certification mark owned by Bluetooth SIG, INC. The user
equipment devices may also communicate with each other directly through an
indirect
path via communications network 714.
[0075] System 700 includes content source 716 and media guidance data source
718
coupled to communications network 714 via communication paths 720 and 722,
respectively. Paths 720 and 722 may include any of the communication paths
described
above in connection with paths 708, 710, and 712. Communications with the
content
source 716 and media guidance data source 718 may be exchanged over one or
more
communication paths but are shown as a single path in FIG. 7 to avoid
overcomplicating
the drawing. In addition, there may be more than one of each of content source
716 and
media guidance data source 718, but only one of each is shown in FIG. 7 to
avoid
overcomplicating the drawing. (The different types of each of these sources
are
discussed below.) If desired, content source 716 and media guidance data
source 718
may be integrated as one source device. Although communications between
sources 716
and 718 with user equipment devices 702, 704, and 706 are shown as through
communications network 714, in some embodiments, sources 716 and 718 may
communicate directly with user equipment devices 702, 704, and 706 via
communication paths (not shown) such as those described above in connection
with
paths 708, 710, and 712.
[0076] Content source 716 may include one or more types of content
distribution
equipment including a television distribution facility, cable system headend,
satellite
distribution facility, programming sources (e.g., television broadcasters,
such as NBC,
ABC, HBO, etc.), intermediate distribution facilities and/or servers, Internet
providers,
on-demand media servers, and other content providers. NBC is a trademark owned
by
the National Broadcasting Company, Inc., ABC is a trademark owned by the
American
Broadcasting Company, Inc., and HBO is a trademark owned by the Home Box
Office,
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Inc. Content source 716 may be the originator of content (e.g., a television
broadcaster, a
Webcast provider, etc.) or may not be the originator of content (e.g., an on-
demand
content provider, an Internet provider of content of broadcast programs for
downloading,
etc.). Content source 716 may include cable sources, satellite providers, on-
demand
providers, Internet providers, over-the-top content providers, or other
providers of
content. Content source 716 may also include a remote media server used to
store
different types of content (including video content selected by a user), in a
location
remote from any of the user equipment devices. Systems and methods for remote
storage
of content, and providing remotely stored content to user equipment are
discussed in
greater detail in connection with Ellis et al., U.S. Patent No. 7,761,892,
issued July 20,
2010, which is hereby incorporated by reference herein in its entirety.
[0077] Media guidance data source 718 may provide media guidance data, such as
the
media guidance data described above. Media guidance data may be provided to
the user
equipment devices using any suitable approach. In some embodiments, the
guidance
application may be a stand-alone interactive television program guide that
receives
program guide data via a data feed (e.g., a continuous feed or trickle feed).
Program
schedule data and other guidance data may be provided to the user equipment on
a
television channel sideband, using an in-band digital signal, using an out-of-
band digital
signal, or by any other suitable data transmission technique. Program schedule
data and
other media guidance data may be provided to user equipment on multiple analog
or
digital television channels.
[0078] In some embodiments, guidance data from media guidance data source 718
may be provided to users' equipment using a client-server approach. For
example, a user
equipment device may pull media guidance data from a server, or a server may
push
media guidance data to a user equipment device. In some embodiments, a
guidance
application client residing on the user's equipment may initiate sessions with
source 718
to obtain guidance data when needed (e.g. when the guidance data is out of
date or when
the user equipment device receives a request from the user to receive data).
Media
guidance may be provided to the user equipment with any suitable frequency
(e.g.,
continuously, daily, a user-specified period of time, a system-specified
period of time, in
response to a request from user equipment, etc.). Media guidance data source
718 may
provide user equipment devices 702, 704, and 706 the media guidance
application itself
or software updates for the media guidance application.
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[0079] In some embodiments, the media guidance data may include viewer data.
For
example, the viewer data may include current and/or historical user activity
information
(e.g., what content the user typically watches, what times of day the user
watches
content, whether the user interacts with a social network, at what times the
user interacts
with a social network to post information, what types of content the user
typically
watches (e.g., pay TV or free TV), mood, brain activity information, etc.).
The media
guidance data may also include subscription data. For example, the
subscription data
may identify to which sources or services a given user subscribes and/or to
which
sources or services the given user has previously subscribed but later
terminated access
(e.g., whether the user subscribes to premium channels, whether the user has
added a
premium level of services, whether the user has increased Internet speed). In
some
embodiments, the viewer data and/or the subscription data may identify
patterns of a
given user for a period of more than one year. The media guidance data may
include a
model (e.g., a survivor model) used for generating a score that indicates a
likelihood a
given user will terminate access to a service/source. For example, the media
guidance
application may process the viewer data with the subscription data using the
model to
generate a value or score that indicates a likelihood of whether the given
user will
terminate access to a particular service or source. In particular, a higher
score may
indicate a higher level of confidence that the user will terminate access to a
particular
service or source. Based on the score, the media guidance application may
generate
promotions that entice the user to keep the particular service or source
indicated by the
score as one to which the user will likely terminate access.
[0080] Media guidance applications may be, for example, stand-alone
applications
implemented on user equipment devices. For example, the media guidance
application
may be implemented as software or a set of executable instructions which may
be stored
in storage 608 and executed by control circuitry 604 of a user equipment
device 600. In
some embodiments, media guidance applications may be client-server
applications
where only a client application resides on the user equipment device, and
server
application resides on a remote server. For example, media guidance
applications may
be implemented partially as a client application on control circuitry 604 of
user
equipment device 600 and partially on a remote server as a server application
(e.g.,
media guidance data source 718) running on control circuitry of the remote
server.
When executed by control circuitry of the remote server (such as media
guidance data
source 718), the media guidance application may instruct the control circuitry
to
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generate the guidance application displays and transmit the generated displays
to the
user equipment devices. The server application may instruct the control
circuitry of the
media guidance data source 718 to transmit data for storage on the user
equipment. The
client application may instruct control circuitry of the receiving user
equipment to
generate the guidance application displays.
[0081] Content and/or media guidance data delivered to user equipment devices
702,
704, and 706 may be over-the-top (OTT) content. OTT content delivery allows
Internet-
enabled user devices, including any user equipment device described above, to
receive
content that is transferred over the Internet, including any content described
above, in
addition to content received over cable or satellite connections. OTT content
is
delivered via an Internet connection provided by an Internet service provider
(ISP), but a
third party distributes the content. The ISP may not be responsible for the
viewing
abilities, copyrights, or redistribution of the content, and may only transfer
IP packets
provided by the OTT content provider. Examples of OTT content providers
include
YOUTUBE, NETFLIX, and HULU, which provide audio and video via IP packets.
Youtube is a trademark owned by Google Inc., Netflix is a trademark owned by
Netflix
Inc., and Hulu is a trademark owned by Hulu, LLC. OTT content providers may
additionally or alternatively provide media guidance data described above. In
addition to
content and/or media guidance data, providers of OTT content can distribute
media
guidance applications (e.g., web-based applications or cloud-based
applications), or the
content can be displayed by media guidance applications stored on the user
equipment
device.
[0082] Media guidance system 700 is intended to illustrate a number of
approaches, or
network configurations, by which user equipment devices and sources of content
and
guidance data may communicate with each other for the purpose of accessing
content
and providing media guidance. The embodiments described herein may be applied
in
any one or a subset of these approaches, or in a system employing other
approaches for
delivering content and providing media guidance. The following four approaches
provide specific illustrations of the generalized example of FIG. 5.
[0083] In one approach, user equipment devices may communicate with each other
within a home network. User equipment devices can communicate with each other
directly via short-range point-to-point communication schemes described above,
via
indirect paths through a hub or other similar device provided on a home
network, or via
communications network 714. Each of the multiple individuals in a single home
may
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operate different user equipment devices on the home network. As a result, it
may be
desirable for various media guidance information or settings to be
communicated
between the different user equipment devices. For example, it may be desirable
for
users to maintain consistent media guidance application settings on different
user
equipment devices within a home network, as described in greater detail in
Ellis et al.,
U.S. Patent Publication No. 2005/0251827, filed July 11,2005. Different types
of user
equipment devices in a home network may also communicate with each other to
transmit
content. For example, a user may transmit content from user computer equipment
to a
portable video player or portable music player.
[0084] In a second approach, users may have multiple types of user equipment
by
which they access content and obtain media guidance. For example, some users
may
have home networks that are accessed by in-home and mobile devices. Users may
control in-home devices via a media guidance application implemented on a
remote
device. For example, users may access an online media guidance application on
a
website via a personal computer at their office, or a mobile device such as a
PDA or
web-enabled mobile telephone. The user may set various settings (e.g.,
recordings,
reminders, or other settings) on the online guidance application to control
the user's in-
home equipment. The online guide may control the user's equipment directly, or
by
communicating with a media guidance application on the user's in-home
equipment.
Various systems and methods for user equipment devices communicating, where
the
user equipment devices are in locations remote from each other, is discussed
in, for
example, Ellis et al., U.S. Patent No. 8,046,801, issued October 25, 2011,
which is
hereby incorporated by reference herein in its entirety.
[0085] In a third approach, users of user equipment devices inside and outside
a home
can use their media guidance application to communicate directly with content
source
716 to access content. Specifically, within a home, users of user television
equipment
702 and user computer equipment 704 may access the media guidance application
to
navigate among and locate desirable content. Users may also access the media
guidance
application outside of the home using wireless user communications devices 706
to
navigate among and locate desirable content.
[0086] In a fourth approach, user equipment devices may operate in a cloud
computing
environment to access cloud services. In a cloud computing environment,
various types
of computing services for content sharing, storage or distribution (e.g.,
video sharing
sites or social networking sites) are provided by a collection of network-
accessible
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computing and storage resources, referred to as "the cloud." For example, the
cloud can
include a collection of server computing devices, which may be located
centrally or at
distributed locations, that provide cloud-based services to various types of
users and
devices connected via a network such as the Internet via communications
network 714.
These cloud resources may include one or more content sources 716 and one or
more
media guidance data sources 718. In addition or in the alternative, the remote
computing
sites may include other user equipment devices, such as user television
equipment 702,
user computer equipment 704, and wireless user communications device 706. For
example, the other user equipment devices may provide access to a stored copy
of a
video or a streamed video. In such embodiments, user equipment devices may
operate
in a peer-to-peer manner without communicating with a central server.
[0087] The cloud provides access to services, such as content storage, content
sharing,
or social networking services, among other examples, as well as access to any
content
described above, for user equipment devices. Services can be provided in the
cloud
through cloud computing service providers, or through other providers of
online
services. For example, the cloud-based services can include a content storage
service, a
content sharing site, a social networking site, or other services via which
user-sourced
content is distributed for viewing by others on connected devices. These cloud-
based
services may allow a user equipment device to store content to the cloud and
to receive
content from the cloud rather than storing content locally and accessing
locally-stored
content.
[0088] A user may use various content capture devices, such as camcorders,
digital
cameras with video mode, audio recorders, mobile phones, and handheld
computing
devices, to record content. The user can upload content to a content storage
service on
the cloud either directly, for example, from user computer equipment 704 or
wireless
user communications device 706 having content capture feature. Alternatively,
the user
can first transfer the content to a user equipment device, such as user
computer
equipment 704. The user equipment device storing the content uploads the
content to the
cloud using a data transmission service on communications network 714. In some
embodiments, the user equipment device itself is a cloud resource, and other
user
equipment devices can access the content directly from the user equipment
device on
which the user stored the content.
[0089] Cloud resources may be accessed by a user equipment device using, for
example, a web browser, a media guidance application, a desktop application, a
mobile
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application, and/or any combination of access applications of the same. The
user
equipment device may be a cloud client that relies on cloud computing for
application
delivery, or the user equipment device may have some functionality without
access to
cloud resources. For example, some applications running on the user equipment
device
.. may be cloud applications (i.e. applications delivered as a service over
the Internet)
while other applications may be stored and run on the user equipment device.
In some
embodiments, a user device may receive content from multiple cloud resources
simultaneously. For example, a user device can stream audio from one cloud
resource
while downloading content from a second cloud resource. Or a user device can
download content from multiple cloud resources for more efficient downloading.
In
some embodiments, user equipment devices can use cloud resources for
processing
operations such as the processing operations performed by processing circuitry
described in relation to FIG. 6. In such embodiments, user equipment devices
may be
connected to a cloud entertainment service system (e.g. cloud gaming
consoles).
[0090] FIG. 8 is a flowchart of a process 800 for providing content
recommendations
using a personalized model, in accordance with some embodiments of the
disclosure.
Process 800, and any of the following processes, may be executed by control
circuitry
(e.g. by instructing control circuitry 504 in a recommendations engine 502).
The control
circuitry may be part of a recommendations engine (e.g. recommendations engine
502)
.. or may be part of a remote server separate from the recommendations engine
by way of
a communications network or distributed over a combination of both. A system
(e.g.
system 200) may perform process 800 as described herein.
[0091] At 802, a trained model (e.g. trained model 206) for generating content
recommendations is provided (e.g. by recommendations engine 202). The trained
model
may have been trained using a predefined set of training data. In some
embodiments,
the training data may be agnostic to a specific user profile.
[0092] At 804, information corresponding to content consumption may be
received
(e.g. by system 200). The information may be content consumption data. In some
embodiments, the information may include content metadata. Content metadata
may
indicate time, location, content type, and/or content genre of consumed
content. The
information may also include control activity data. Control activity data may
indicate
one or more control function selections made while consuming content. In some
embodiments, the information may include information corresponding to portions
of
consumed content. In some embodiments, the information may include content
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consumption data on a social network. Some examples are shown in FIG. 4 as
content
consumption data 404 which includes data 406 through 416.
[0093] At 806, the information may be associated with a profile (e.g. by
recommendations engine 202 and/or a media guidance application). The profile
may be
associated with a user. For example, the content consumption data may be
linked to the
profile. In this manner, a system may consider the consumption preferences of
a
particular profile data. Any content recommendations generated by a system may
be
associated to the particular profile data.
[0094] At 808, an updated model may be generated based on the information
corresponding to content consumption and on the provided model (e.g. by
recommendations engine 202). The updated model may also be referred to as a
personalized model, which is personalized to a user associated with the
information
corresponding to content consumption. In some embodiments, the personalized
model
may be a first updated model. In some embodiments, the personalized model may
be
associated with a profile. For example, the personalized model may be linked
to the
profile. A system may use the personalized model whenever the linked profile
requests
content as described at 810.
[0095] At 810, content recommendations may be generated using a personalized
model (e.g. by recommendations engine 202). The content recommendations may
include previously consumed and unconsumed content. The recommendations may be
provided at a time when the user may enjoy consuming the content.
[0096] At 812, a system may cause to be provided the content recommendations
to a
user (e.g. executing instructions via control circuitry 504 to transmit to
user equipment
214 by communications circuitry 508). In some embodiments, the content
recommendations may be a first set of content recommendations as described in
FIG. 9.
[0097] FIG. 9 is a flowchart of a process 900 for providing content
recommendations
using an updated personalized model, in accordance with some embodiments of
the
disclosure. In some embodiments, process 900 may be considered as a
continuation of
process 800. In some embodiments, at least parts of process 800 and 900 may be
considered as part of a feedback loop (e.g. loop 218). A system (e.g. system
300) may
perform process 900 as described herein.
[0098] At 902, additional information corresponding to content consumption may
be
received (e.g. by system 300). In some embodiments, the additional information
may
include activity corresponding to content recommendations provided by user
equipment
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(e.g. user equipment devices 304). Additionally or alternatively, the
additional
information may include activity corresponding to other content
recommendations
consumed by a user.
[0099] At 904, a second updated model may be generated based on the additional
information and on the first updated model. In some embodiments, the second
updated
model is a personalized model (e.g. updated personalized model 308), which is
updated
using the additional information corresponding to content consumption. The
second
updated model may be referred to as a second personalized model and/or an
updated
personalized model. The personalized model may be updated any number of times
and
based on any suitable trigger (e.g., periodically, by user request, when the
system
determines that particular previously unknown preferences of the user are
significant
enough, etc.). The personalized model may be referred to, herein, as first,
second, third,
etc. updated personalized model (or personalized model for brevity) based on
the
number of times the personalized model has been updated.
[0100] At 906, the second updated model may be used to generate additional
content
recommendations. For example, the system may use the second personalized model
to
generate recommendations similar to any content item in the content
consumption data.
The additional content recommendations may be a second set of content
recommendations (e.g. content recommendations 310). A system (e.g. system 300)
may
cause to be provided the second content recommendations to user equipment.
[0101] FIG. 10 is a flowchart of a process 1000 for generating a personalized
model
including a genre ranking, in accordance with some embodiments of the
disclosure.
Process 1000 may be part of process 900, and/or any other process described in
the
present disclosure, when generating content recommendations based on content
consumption data. Process 1000 may be performed by a system (e.g. system 400,
where
content consumption data 404 includes data 406-416). For simplicity, the
content
ranking described in relation to process 1000 herein is based on content
genres and/or
themes, but it should be noted that the content ranking may be based on one or
more
criteria of content consumption, including content genres and/or themes.
[0102] Content consumption data 1002 may be provided. Content consumption data
1002 may include consumed content genres and/or themes indicated by content
metadata
(or other data indicative of consumed content genres). Content consumption
data 1002
may be indicative of preferred content genres and/or consumed by a user. At
1006,
content genres may be ranked based on the content consumption data to generate
a
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content ranking. The genre ranking may order content genres, for example, from
most
preferred to least preferred content genres. Additionally or alternatively,
the genre
ranking may order content genres from least preferred to most preferred.
[0103] At 1008, a second updated model may be generated based on the
personalized
model (e.g. personalized model 210) and the genre ranking (e.g. personalized
model
402). The second updated model may include updating one or more weights of a
first
updated model as described in FIG. 11.
[0104] At 1010, an ordered set of content recommendations may be generated
using
the second updated model based on the genre ranking (e.g. ranked content
recommendations 420). For example, the ordering of the recommendations may be
from
most preferred genres to least preferred genres. For example, a system may
recommend
content such as Lord of the Rings (fantasy), the Tudors (drama), and Spartacus
(action).
Content consumption data may have indicated a user prefers action over fantasy
and
fantasy over drama. In this non-limiting example, the system may generate a
preferred
genre ranking of 1) action, 2) fantasy, and 3) drama. The system may cause to
provide
the content recommendations in the order of 1) Spartacus, 2) Lord of the
Rings, and 3)
Tudors.
[0105] FIG. 11 is a flowchart of a process 1100 for generating a personalized
model
based on state data and optimized weights, in accordance with some embodiments
of the
disclosure. Process 1100 may be performed by any system described in the
present
disclosure involving generating an updated model. Process 1100 may generate
the
updated model using a long short-term memory recurrent neural network (LSTM
RNN)
model. For simplicity, process 1100 is described in the context of a LSTM RNN
model.
It should be noted that process 1100 described herein may be performed by a
system
using any applicable model including variants of a LSTM RNN model. It should
also be
noted that, while primarily machine learning techniques are described, any
appropriate
techniques for computations, optimizations, etc. may be used in performing
process
1100 as alternative or additional parts of the process.
[0106] One skilled in the art may consider a long short-term memory recurrent
neural
network as a RNN with the inclusion of a cell state (e.g. a state variable
stored in
memory). The cell state may store content consumption data as numerical data
or other
representation. The cell state may be used to determine how to update the
model based
on content consumption data and other input data. The cell state may also be
used to
determine which weights to update based on content consumption data and other
input
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data. The cell state may also be used to generate updated or optimized weights
based on
content consumption data and other input data.
For example, control function activity while watching Game of Thrones may
indicate
enjoyment of a particular scene such as an action scene. For example, a user
may have
replayed the action scene using a remote controller, voice control, or other
control
device and method. A cell state may have already included data indicative of
the user
watching Game of Thrones but may not include the replay activity of the
particular
scene. Then, a system may determine to update the state (or states)
corresponding to
replay activity of that particular scene.
.. [0107] A model (e.g. a LSTM RNN model) may indicate preferences or other
criteria
corresponding to content consumption by one or more sets of weights. The
weights may
represent the preferences or other criteria using numerical data or other
formats. The
weights may be associated with any of the models described in the present
disclosure
and based on content consumption data. As indicated, the weights may be
determined
and/or optimized using machine learning techniques, or any other relevant
techniques,
such as combining gradient descent and backpropagation. It is preferable that
a
personalized model is generated based on a set of optimized weights and one or
more
cell states with respect to the preferences of a user.
[0108] At 1102, one or more cell states may be determined based on weights
associated with a trained model (or any other model described in the present
disclosure)
and content consumption data. For example, a user may have replayed the action
scene
using a remote controller, voice control, or other control device and method.
A system
may determine to update the states related to replay of an action scene and
the associated
weights. For example, recommendations engine 202 may determine the cell states
based
on content consumption data 208 and trained model 206 in order to generate
personalized model 210, which may be based on a LSTM RNN model. In some
embodiments, a recommendations engine may combine training data, content
consumption data, and a trained model in a single step to generate a
personalized model.
[0109] At 1104, one or more sets of weights may be determined based on the
cell
states. The weights may be determined based on all or part of the cell states.
For
example, a system may determine from the cell state to update only some of the
weights
related to replay of an action scene. Updating the weights may involve machine
learning
techniques such as gradient descent.
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[0110] At 1106, a system may determine whether the one or more weights are
optimized. It is preferred that one or more weights are optimized with respect
to
preferences associated with content consumption data. For example, the weights
may
indicate incorrect predictions in preferred content (i.e. have a high error
value). If the
weights have high error, a system may determine the weights are not optimized
and
return to 1102. Optimization techniques such as back propagation may be used
to
reduce the error in a loop (e.g. loop including 1102, 1104, and 1106) and
update the
weights and states until the weights are optimized. If the weights are
determined to be
optimized, process 1100 continues to 1108.
[0111] At 1108, an updated model is generated based on the set of optimized
weights
and the one or more states. In some embodiments, a LSTM RNN model may be
generated where the cell states store all the content consumption data and the
weights
may be used to determine the criteria for content recommendations. In this
manner, a
recommendations engine may generate content recommendations by determining
whether a content item matches the criteria based on the weights.
[0112] FIG. 12 shows an illustrative block diagram of a system 1200 for
providing
content portion recommendations using a trained model, in accordance with some
embodiments of the disclosure. System 1200 may provide a trained model 1202.
In
some embodiments, trained model 1202 may have been updated based on
information
about content consumption associated with a profile. The information about
content
consumption may include information about consumption of portions of content
items
(i.e. content portion consumption data 1204). System 1200 may generate content
recommendations using trained model 1202. Additionally or alternatively,
system 1200
may generate content portion recommendations 1206 based on the generated
content
recommendations and on the information about consumption of portions of
content
items (i.e. content portion consumption data 1204). Content portion
recommendations
1206 may include recommendations based on content genre 1208 and/or
recommendations based on content item length 1210. System 1200 may provide
content
portion recommendations 1206 to user equipment 1212. For example, Lord of the
Rings
may be recommended using any of the models described in the present disclosure
based
on content consumption data. System 1200 may generate and provide scene
recommendations from Lord of the Rings and/or a similar series like Game of
Thrones
based on the content consumption data.
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[0113] FIG. 13 shows a flowchart of a process 1300 for providing content
portion
recommendations based on content recommendations, in accordance with some
embodiments of the disclosure. Process 1300 may be performed by any of the
systems
(e.g. system 1200) described in the present disclosure.
[0114] At 1302, a trained model (e.g. trained model 1202) for generating
content
recommendations may be provided (e.g. by recommendations engine 202). The
trained
model may have been updated based on information about content consumption
associated with a profile. The information about content consumption may
include
content portion consumption data 1304. At 1306, the trained model may be used
to
.. generate content recommendations. For example, system 200 may generate
content
recommendations 212. At 1308, a system may generate content portion
recommendations based on the content recommendations and on the information
about
consumption of portions of content items (e.g. content portion consumption
data 1304).
At 1310, a system may provide or cause to be provided the content portion
recommendations. For example, system 1200 may execute instructions via control
circuitry to transmit recommendations 1206 to user equipment 1212.
[0115] FIG. 14 shows a flowchart of a process 1400 for generating content
portion
recommendations based on one or more preferred and/or nonpreferred portions of
a
content item, in accordance with some embodiments of the disclosure Process
1400
may be performed by any of the systems (e.g. system 1200) described in the
present
disclosure.
[0116] At 1402, a preferred portion of a content item may be determined based
on
information about consumption of portions of content items (i.e. content
portion
consumption data 1404). For example, a user prefers watching a battle scene
from Lord
of the Rings based on content portion consumption data 1404. In some
embodiments, a
system may determine a preferred portion of a content item based on user
activity
corresponding to the content item. In some embodiments, a system may determine
a
portion of a content item based on consumption data of one or more content
items
associated with information about content consumption. The content items may
include
the content item including the portion and/or other content items. For
example, the other
content items may be similar to a portion of the content item. A system may
identify the
portion of the content item based on similarity of other consumed content with
the
portion of the content item. Determining the similarity may be based on
various metrics
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such as numerical data (e.g. a similarity score), content metadata, or other
data indicative
of the similarity.
[0117] At 1406, content portion recommendations may be generated based on one
or
more preferred and/or nonpreferred portions of content items. In some
embodiments, a
__ consumption preference may be determined based on preferred and/or
nonpreferred
portions. The consumption preference may be associated with a profile. In some
embodiments, a consumption preference may be determined based on consumption
activity information associated with a portion. For example, content
consumption data
1204 may indicate activity of a scene from Lord of the Rings being watched
multiple
times. In some embodiments, a consumption preference may be determined based
on
information about content consumption including content genres, content
lengths, time
of content consumption, location of content consumption, content type, and/or
control
function selection made during content consumption. Content consumption
preference
may include preference of content lengths, time of content consumption,
location of
content consumption, content type, and/or control function selection made
during
content consumption.
[0118] In some embodiments, a trained or personalized model may be updated
based
on preferred and/or nonpreferred portions. In some embodiments, a trained or
personalized model may be used to generate content portion recommendations
based on
preferred and/or nonpreferred portions. In some embodiments, the content
recommendations and/or content portion modifications may be generated as
modified
recommendations based on a preferred or a nonpreferred portion.
[0119] In some embodiments, a trained model may be provided that has been
updated
based on information about content consumption associated with a profile. The
information about content consumption may include information about
consumption of
portions of content items. A system may generate content recommendations using
the
trained model. In some embodiments, the system may generate content portion
recommendations based on content recommendations and on information about
consumption of portions of content items. The system may provide the content
portion
recommendations. For example, Lord of the Rings may be recommended using any
of
the models described in the present disclosure based on content consumption
data. A
system may generate and provide scene recommendations from Lord of the Rings
and/or
a similar series like Game of Thrones based on the content consumption data.
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[0120] As referred herein, the term "in response to" refers to initiated as a
result of
For example, a first action being performed in response to a second action may
include
interstitial steps between the first action and the second action. As referred
herein, the
term "directly in response to" refers to caused by. For example, a first
action being
performed directly in response to a second action may not include interstitial
steps
between the first action and the second action.
[0121] The systems and processes discussed above are intended to be
illustrative and
not limiting. One skilled in the art would appreciate that the actions of the
processes
discussed herein may be omitted, modified, combined, and/or rearranged, and
any
additional actions may be performed without departing from the scope of the
invention.
More generally, the above disclosure is meant to be exemplary and not
limiting. Only
the claims that follow are meant to set bounds as to what the present
disclosure includes.
Furthermore, it should be noted that the features and limitations described in
any one
embodiment may be applied to any other embodiment herein, and flowcharts or
examples relating to one embodiment may be combined with any other embodiment
in a
suitable manner, done in different orders, or done in parallel. In addition,
the systems
and methods described herein may be performed in real time. It should also be
noted
that the systems and/or methods described above may be applied to, or used in
accordance with, other systems and/or methods.
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This specification discloses embodiments which include, but are not limited
to, the
following:
1. A computer-implemented method of providing a content recommendation,
the
method comprising:
providing a trained model to provide content recommendations, the trained
model having been trained using a predefined set of training data;
receiving information corresponding to content consumption;
associating the information corresponding to content consumption with a
profile;
generating, using processing circuitry, an updated model based on the
information and on the trained model, wherein the updated model is associated
with the
profile;
generating the content recommendations using the updated model; and
causing to be provided the content recommendations.
2. The method of item 1, wherein the updated model is a first updated model
and
the content recommendations are first content recommendations, the method
further
comprising:
receiving additional information corresponding to consumption of the first
content recommendations;
generating, using processing circuitry, a second updated model based on the
additional information and on the first updated model, wherein the second
updated
model is associated with the profile;
generating second content recommendations using the second updated model;
and
causing to be provided the second content recommendations.
3. The method of item 1, wherein the predefined set of training data is
agnostic of
the profile associated with the user.
4. The method of item 1, wherein generating the content recommendations
comprises generating recommendations of one or more portions of a content item
using
the updated model.
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5. The method of item 1, wherein the information corresponding to content
consumption is based on at least one of a time of consumption, a location of
consumption, a genre of content consumed, a type of content consumed, or a
control
function selection made during content consumption.
6. The method of item 5, wherein the updated model is a first updated
model, and
wherein the content recommendations are first content recommendations, the
method
further comprising:
ranking content genres contained in the information corresponding to content
consumption to generate a genre ranking;
generating a second updated model based on the first updated model and on the
genre ranking; and
generating second content recommendations using the second updated model,
wherein ordering of the second content recommendations is based on the genre
ranking.
7. The method of item 1, wherein the information corresponding to content
consumption is based on at least one of full consumption of content, partial
consumption
of content, or frequency of consumption of content.
8. The method of item 1, wherein generating the updated model comprises
generating a model using a long short-term memory recurrent neural network
(LSTM
RNN).
9. The method of item 8, wherein generating the model using the LSTM RNN
comprises:
determining one or more states iteratively based on one or more sets of
weights
associated with the trained model and based on the information corresponding
to content
consumption;
determining one or more sets of optimized weights; and
generating the model based on the one or more sets of optimized weights and on
the one or more states.
10. The method of item 1, wherein the information corresponding to content
consumption comprises information about activity on a social network.
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11. A system for providing a content recommendation, the system comprising:
control circuitry configured to:
provide a trained model to provide content recommendations, the trained
model having been trained using a predefined set of training data;
receive information corresponding to content consumption;
associate the information corresponding to content consumption with a
profile;
processing circuitry configured to:
generate an updated model based on the information corresponding to
content consumption and on the trained model, wherein the updated model is
associated
with the profile; and
wherein the control circuitry is further configured to:
generate the content recommendations using the updated model; and
cause to be provided the content recommendations.
12. The system of item 11, wherein the updated model is a first updated
model and
the content recommendations are first content recommendations, and wherein:
the control circuitry is further configured to:
receive additional information corresponding to consumption of the first
content recommendations;
the processing circuitry is further configured to:
generate a second updated model based on the additional information and
on the first updated model, wherein the second updated model is associated
with the
profile; and
the control circuitry is further configured to:
generate second content recommendations using the second updated
model; and
cause to be provided the second content recommendations.
13. The system of item 11, wherein the predefined set of training data is
agnostic of
the profile associated with the user.
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14. The system of item 11, wherein the control circuitry is configured
to generate the
content recommendations by generating recommendations of one or more portions
of a
content item using the updated model.
15. The system of item 11, wherein the information corresponding to content
consumption is based on at least one of a time of consumption, a location of
consumption, a genre of content consumed, a type of content consumed, or a
control
function selection made during content consumption.
16. The system of item 15, wherein the updated model is a first updated
model,
wherein the content recommendations are first content recommendations, and
wherein:
the control circuitry is further configured to rank content genres contained
in the
information corresponding to content consumption to generate a genre ranking;
the processing circuitry is further configured to generate a second updated
model
based on the first updated model and on the genre ranking; and
the control circuitry is further configured to generate second content
recommendations using the second updated model, wherein ordering of the second
content recommendations is based on the genre ranking
17. The system of item 11, wherein the information corresponding to content
consumption is based on at least one of full consumption of content, partial
consumption
of content, or frequency of consumption of content.
18. The system of item 11, wherein the processing circuitry is configured
to generate
the updated model by generating a model using a long short-term memory
recurrent
neural network (LSTM RNN).
19. The system of item 18, wherein generating the model using the LSTM RNN
comprises:
determining one or more states iteratively based on one or more sets of
weights
associated with the trained model and based on the information corresponding
to content
consumption;
determining one or more sets of optimized weights; and
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generating the model based on the one or more sets of optimized weights and on
the one or more states.
20. The system of item 11, wherein the information corresponding to content
consumption comprises information about activity on a social network.
21. A system for providing a content recommendation, the system comprising:
means for providing a trained model to provide content recommendations, the
trained model having been trained using a predefined set of training data;
means for receiving information corresponding to content consumption;
means for associating the information corresponding to content consumption
with a profile;
means for generating, using processing circuitry, an updated model based on
the
information corresponding to content consumption and on the trained model,
wherein
the updated model is associated with the profile;
means for generating the content recommendations using the updated model; and
means for causing to be provided the content recommendations.
22. The system of item 21, wherein the updated model is a first updated
model and
the content recommendations are first content recommendations, the system
further
comprising:
means for receiving additional information corresponding to consumption of the
first content recommendations;
means for generating a second updated model based on the additional
information and on the first updated model, wherein the second updated model
is
associated with the profile;
means for generating second content recommendations using the second updated
model; and
means for causing to be provided the second content recommendations.
23. The system of item 21, wherein the predefined set of training data is
agnostic of
the profile associated with the user.
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24. The system of item 21, wherein means for generating the content
recommendations comprises means for generating recommendations of one or more
portions of a content item using the updated model.
25. The system of item 21, wherein the information corresponding to content
consumption is based on at least one of a time of consumption, a location of
consumption, a genre of content consumed, a type of content consumed, or a
control
function selection made during content consumption.
26. The system of item 25, wherein the updated model is a first updated
model, and
wherein the content recommendations are first content recommendations, the
system
further comprising:
means for ranking content genres contained in the information corresponding to
content consumption to generate a genre ranking;
means for generating a second updated model based on the first updated model
and on the genre ranking; and
means for generating second content recommendations using the second updated
model, wherein ordering of the second content recommendations is based on the
genre
ranking.
27. The system of item 21, wherein the information corresponding to
content
consumption is based on at least one of full consumption of content, partial
consumption
of content, or frequency of consumption of content.
28. The system of item 21, wherein means for generating the updated model
comprises means for generating a model using a long short-term memory
recurrent
neural network (LSTM RNN).
29. The system of item 28, wherein means for generating the model using
the LSTM
RNN comprises:
means for determining one or more states iteratively based on one or more sets
of
weights associated with the trained model and based on the information
corresponding
to content consumption;
means for determining one or more sets of optimized weights; and
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means for generating the model based on the one or more sets of optimized
weights and on the one or more states.
30. The system of item 21, wherein the information corresponding to content
consumption comprises information about activity on a social network.
31. A non-transitory computer-readable medium having instructions encoded
thereon
that when executed by control circuitry cause the control circuitry to:
provide a trained model to provide content recommendations, the trained model
having been trained using a predefined set of training data;
receive information corresponding to content consumption;
associate the information corresponding to content consumption with a profile;
generate, using processing circuitry, an updated model based on the
information
corresponding to content consumption and on the trained model, wherein the
updated
model is associated with the profile;
generate the content recommendations using the updated model; and
cause to be provided the content recommendations.
32. The non-transitory computer readable medium of item 31, wherein the
instructions cause the control circuitry to further:
receive additional information corresponding to consumption of the first
content
recommendations;
generate, using processing circuitry, a second updated model based on the
additional information and on the first updated model, wherein the second
updated
model is associated with the profile;
generate second content recommendations using the second updated model; and
cause to be provided the second content recommendations.
33. The non-transitory computer readable medium of item 31, wherein the
predefined set of training data is agnostic of the profile associated with the
user.
34. The non-transitory computer readable medium of item 31, wherein the
instructions for generating the content recommendations cause the control
circuitry to
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generate recommendations of one or more portions of a content item using the
updated
model.
35. The non-transitory computer readable medium of item 31, wherein the
information corresponding to content consumption is based on at least one of a
time of
consumption, a location of consumption, a genre of content consumed, a type of
content
consumed, or a control function selection made during content consumption.
36. The non-transitory computer readable medium of item 35, wherein the
updated
model is a first updated model, and wherein the content recommendations are
first
content recommendations, and wherein the instructions cause the control
circuitry to
further:
receive additional information corresponding to consumption of the first
content
recommendations;
generate, using processing circuitry, a second updated model based on the
additional information and on the first updated model, wherein the second
updated
model is associated with the profile; and
generate second content recommendations using the second updated model; and
cause to be provided the second content recommendations.
37. The non-transitory computer readable medium of item 31, wherein the
information corresponding to content consumption is based on at least one of
full
consumption of content, partial consumption of content, or frequency of
consumption of
content.
38. The non-transitory computer readable medium of item 31, wherein the
instructions for generating the updated model cause the control circuitry to
generate a
model using a long short-term memory recurrent neural network (LSTM RNN).
39. The non-transitory computer readable medium of item 38, wherein the
instructions for generating the model using the LSTM RNN cause the control
circuitry
to:
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determine one or more states iteratively based on one or more sets of weights
associated with the trained model and based on the information corresponding
to content
consumption;
determine one or more sets of optimized weights; and
generate the model based on the one or more sets of optimized weights and on
the one or more states.
40. The non-transitory computer readable medium of item 31, wherein the
information corresponding to content consumption comprises information about
activity
on a social network
41. A computer-implemented method of providing a content recommendation,
the
method comprising:
providing a trained model to provide content recommendations, the trained
model having been trained using a predefined set of training data;
receiving information corresponding to content consumption;
associating the information corresponding to content consumption with a
profile;
generating, using processing circuitry, an updated model based on the
information corresponding to content consumption and on the trained model,
wherein
the updated model is associated with the profile;
generating the content recommendations using the updated model; and
causing to be provided the content recommendations.
42. The method of item 41, wherein the updated model is a first updated
model and
the content recommendations are first content recommendations, the method
further
comprising:
receiving additional information corresponding to consumption of the first
content recommendations;
generating, using processing circuitry, a second updated model based on the
additional information and on the first updated model, wherein the second
updated
model is associated with the profile;
generating second content recommendations using the second updated model;
and
causing to be provided the second content recommendations.
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43. The method of any of items 41 and 42, wherein the predefined set of
training
data is agnostic of the profile associated with the user.
44. The method of any of items 41-43, wherein generating the content
recommendations comprises generating recommendations of one or more portions
of a
content item using the updated model.
45. The method of any of items 41-44, wherein the information corresponding
to
content consumption is based on at least one of a time of consumption, a
location of
consumption, a genre of content consumed, a type of content consumed, or a
control
function selection made during content consumption.
46. The method of item 45, wherein the updated model is a first updated
model, and
wherein the content recommendations are first content recommendations, the
method
further comprising:
ranking content genres contained in the information corresponding to content
consumption to generate a genre ranking;
generating a second updated model based on the first updated model and on the
genre ranking; and
generating second content recommendations using the second updated model,
wherein ordering of the second content recommendations is based on the genre
ranking.
47. The method of any of items 41-46, wherein the information corresponding
to
content consumption is based on at least one of full consumption of content,
partial
consumption of content, or frequency of consumption of content.
48. The method of any of items 41-47, wherein generating the updated model
comprises generating a model using a long short-term memory recurrent neural
network
(L S TM RNN).
49. The method of item 48, wherein generating the model using the LSTM RNN
comprises:
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determining one or more states iteratively based on one or more sets of
weights
associated with the trained model and based on the information corresponding
to content
consumption;
determining one or more sets of optimized weights; and
generating the model based on the one or more sets of optimized weights and on
the one or more states.
50. The method of any of items 41-49, wherein the information corresponding
to
content consumption comprises information about activity on a social network.
51. A computer-implemented method of providing content recommendations, the
method comprising:
providing a trained model that had been updated based on information about
content consumption associated with a profile, wherein the information about
content
.. consumption comprises information about consumption of portions of content
items;
generating, using the trained model, content recommendations;
generating content portion recommendations based on the content
recommendations and on the information about consumption of portions of
content
items; and
causing to be provided the content portion recommendations.
52. The method of item 51, wherein a portion of a content item is preferred
based on
the information about consumption of portions of content items, and wherein
generating
content portion recommendations is at least partially based on the preferred
portion.
53. The method of item 51, wherein a portion of a content item is not
preferred based
on the information about consumption of portions of content items, and wherein
generating content portion recommendations is at least partially based on the
nonpreferred portion.
54. The method of item 51, further comprising determining a preferred genre
based
on the information about content consumption, and wherein the content portion
recommendations are based on the preferred genre.
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55. The method of item 51, further comprising determining a preferred
content item
length based on the information about content consumption, and wherein the
content
portion recommendations are based on the preferred content item length.
56. The method of item 51, further comprising ranking content genres
contained in
the information about content consumption to generate a genre ranking, and
wherein
ordering of the content portion recommendations is based on the genre ranking.
57. The method of item 51, wherein the content recommendations are first
content
recommendations, and wherein the content portion recommendations are first
content
portion recommendations, the method further comprising:
receiving additional information corresponding to consumption of the content
portion recommendations and of the content recommendations;
generating second content recommendations using the trained model;
generating second content portion recommendations based on the second content
recommendations and on the additional information; and
causing to be provided the second content portion recommendations.
58. The method of item 51, wherein the information about content
consumption is
based on at least one of full consumption of content, partial consumption of
content, or
frequency of consumption of content.
59. The method of item 51, wherein the information about content
consumption is
based on at least one of a time of consumption, a location of consumption, a
genre of
content consumed, a type of content consumed, or a control function selection
made
during content consumption.
60. The method of item 51, wherein the information about content
consumption
comprises information about activity on a social network.
61. A system for providing content recommendations, the system comprising:
communications circuitry configured to:
provide a trained model that had been updated based on information
about content consumption associated with a profile, wherein the information
about
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content consumption comprises information about consumption of portions of
content
items; and
control circuitry configured to:
generate, using the trained model, content recommendations;
generate content portion recommendations based on the content
recommendations and on the information about consumption of portions of
content
items; and
cause to be provided the content portion recommendations.
62. The system of item 61, wherein a portion of a content item is preferred
based on
the information about consumption of portions of content items, and wherein
the control
circuitry is configured to generate content portion recommendations at least
partially
based on the preferred portion.
63. The system of item 61, wherein a portion of a content item is not
preferred based
on the information about consumption of portions of content items, and wherein
the
control circuitry is configured to generate content portion recommendations at
least
partially based on the nonpreferred portion.
64. The system of item 61, wherein the control circuitry is further
configured to
determine a preferred genre based on the information about content
consumption, and
wherein the content portion recommendations are based on the preferred genre.
65. The system of item 61, wherein the control circuitry is further
configured to
determine a preferred content item length based on the information about
content
consumption, and wherein the content portion recommendations are based on the
preferred content item length.
66. The system of item 61, wherein the control circuitry is further
configured to rank
content genres contained in the information about content consumption to
generate a
genre ranking, and wherein ordering of the content portion recommendations is
based on
the genre ranking.
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67. The system of item 61, wherein the content recommendations are first
content
recommendations, wherein the content portion recommendations are first content
portion recommendations, and wherein:
the communications circuitry is further configured to:
receive additional information corresponding to consumption of the
content portion recommendations and of the content recommendations; and
the control circuitry is further configured to:
generate second content recommendations using the trained model;
generate second content portion recommendations based on the second
content recommendations and on the additional information; and
cause to be provided the second content portion recommendations.
68. The system of item 61, wherein the information about content
consumption is
based on at least one of full consumption of content, partial consumption of
content, or
frequency of consumption of content.
69. The system of item 61, wherein the information about content
consumption is
based on at least one of a time of consumption, a location of consumption, a
genre of
content consumed, a type of content consumed, or a control function selection
made
during content consumption.
70. The system of item 61, wherein the information about content
consumption
comprises information about activity on a social network.
71. A system for providing content recommendations, the system comprising:
means for providing a trained model that had been updated based on information
about content consumption associated with a profile, wherein the information
about
content consumption comprises information about consumption of portions of
content
items;
means for generating, using the trained model, content recommendations;
means for generating content portion recommendations based on the content
recommendations and on the information about consumption of portions of
content
items; and
means for causing to be provided the content portion recommendations.
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72. The system of item 71, wherein a portion of a content item is preferred
based on
the information about consumption of portions of content items, and wherein
generating
content portion recommendations is at least partially based on the preferred
portion.
73. The system of item 71, wherein a portion of a content item is not
preferred based
on the information about consumption of portions of content items, and wherein
generating content portion recommendations is at least partially based on the
nonpreferred portion.
74. The system of item 71, further comprising means for determining a
preferred
genre based on the information about content consumption, and wherein the
content
portion recommendations are based on the preferred genre.
75. The system of item 71, further comprising means for determining a
preferred
content item length based on the information about content consumption, and
wherein
the content portion recommendations are based on the preferred content item
length.
76. The system of item 71, further comprising means for ranking content
genres
contained in the information about content consumption to generate a genre
ranking, and
wherein ordering of the content portion recommendations is based on the genre
ranking.
77. The system of item 71, wherein the content recommendations are first
content
recommendations, and wherein the content portion recommendations are first
content
portion recommendations the system further comprising:
means for receiving additional information corresponding to consumption of the
content portion recommendations and of the content recommendations;
means for generating second content recommendations using the trained model;
means for generating second content portion recommendations based on the
second content recommendations and on the additional information; and
means for causing to be provided the second content portion recommendations.
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78. The system of item 71, wherein the information about content
consumption is
based on at least one of full consumption of content, partial consumption of
content, or
frequency of consumption of content.
79. The system of item 71, wherein the information about content
consumption is
based on at least one of a time of consumption, a location of consumption, a
genre of
content consumed, a type of content consumed, or a control function selection
made
during content consumption.
80. The system of item 71, wherein the information about content
consumption
comprises information about activity on a social network.
81. A non-transitory computer-readable medium having instructions encoded
thereon
that when executed by control circuitry cause the control circuitry to:
provide a trained model that had been updated based on information about
content consumption associated with a profile, wherein the information about
content
consumption comprises information about consumption of portions of content
items;
generate, using the trained model, content recommendations;
generate content portion recommendations based on the content
recommendations and on the information about consumption of portions of
content
items; and
cause to be provided the content portion recommendations.
82. The non-transitory computer readable medium of item 81, wherein a
portion of a
content item is preferred based on the information about consumption of
portions of
content items, and wherein the instructions for generating content portion
recommendations is at least partially based on the preferred portion.
83. The non-transitory computer readable medium of item 81, wherein a
portion of a
content item is not preferred based on the information about consumption of
portions of
content items, and wherein generating content portion recommendations is at
least
partially based on the nonpreferred portion.
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84. The non-transitory computer readable medium of item 81, wherein the
instructions cause the control circuitry to further determine a preferred
genre based on
the information about content consumption, and wherein the content portion
recommendations are based on the preferred genre.
85. The non-transitory computer readable medium of item 81, wherein the
instructions cause the control circuitry to further determine a preferred
content item
length based on the information about content consumption, and wherein the
content
portion recommendations are based on the preferred content item length.
86. The non-transitory computer readable medium of item 81, wherein the
instructions cause the control circuitry to further rank content genres
contained in the
information about content consumption to generate a genre ranking, and wherein
ordering of the content portion recommendations is based on the genre ranking.
87. The non-transitory computer readable medium of item 81, wherein the
content
recommendations are first content recommendations, and wherein the content
portion
recommendations are first content portion recommendations, and wherein the
instructions cause the control circuitry to further:
receive additional information corresponding to consumption of the content
portion recommendations and of the content recommendations;
generate second content recommendations using the trained model;
generate second content portion recommendations based on the second content
recommendations and on the additional information; and
cause to be provided the second content portion recommendations.
88. The non-transitory computer readable medium of item 81, wherein the
information about content consumption is based on at least one of full
consumption of
content, partial consumption of content, or frequency of consumption of
content.
89. The non-transitory computer readable medium of item 81, wherein the
information about content consumption is based on at least one of a time of
consumption, a location of consumption, a genre of content consumed, a type of
content
consumed, or a control function selection made during content consumption.
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90. The non-transitory computer readable medium of item 81, wherein the
information about content consumption comprises information about activity on
a social
network.
91. A computer-implemented method of providing content recommendations, the
method comprising:
providing a trained model that had been updated based on information about
content consumption associated with a profile, wherein the information about
content
consumption comprises information about consumption of portions of content
items;
generating, using the trained model, content recommendations;
generating content portion recommendations based on the content
recommendations and on the information about consumption of portions of
content
items; and
causing to be provided the content portion recommendations.
92. The method of item 91, wherein a portion of a content item is preferred
based on
the information about consumption of portions of content items, and wherein
generating
content portion recommendations is at least partially based on the preferred
portion.
93. The method of any of items 91 and 92, wherein a portion of a content
item is not
preferred based on the information about consumption of portions of content
items, and
wherein generating content portion recommendations is at least partially based
on the
nonpreferred portion.
94. The method of any of items 91-93, further comprising determining a
preferred
genre based on the information about content consumption, and wherein the
content
portion recommendations are based on the preferred genre.
95. The method of any of items 91-94, further comprising determining a
preferred
content item length based on the information about content consumption, and
wherein
the content portion recommendations are based on the preferred content item
length.
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96. The method of any of items 91-95, further comprising ranking content
genres
contained in the information about content consumption to generate a genre
ranking, and
wherein ordering of the content portion recommendations is based on the genre
ranking.
97. The method of any of items 91-96, wherein the content recommendations
are
first content recommendations, and wherein the content portion recommendations
are
first content portion recommendations, the method further comprising:
receiving additional information corresponding to consumption of the content
portion recommendations and of the content recommendations;
generating second content recommendations using the trained model;
generating second content portion recommendations based on the second content
recommendations and on the additional information; and
causing to be provided the second content portion recommendations.
98. The method of any of items 91-97, wherein the information about content
consumption is based on at least one of full consumption of content, partial
consumption
of content, or frequency of consumption of content.
99. The method of any of items 91-98, wherein the information about content
consumption is based on at least one of a time of consumption, a location of
consumption, a genre of content consumed, a type of content consumed, or a
control
function selection made during content consumption.
100. The method of any of items 91-99, wherein the information about content
consumption comprises information about activity on a social network.
56
