U.S. patent application number 16/573802 was filed with the patent office on 2021-03-18 for systems and methods for generating music recommendations.
The applicant listed for this patent is Facebook, Inc.. Invention is credited to Bokai Cao, Parth Popatlal Detroja, Amit Singh.
Application Number | 20210082471 16/573802 |
Document ID | / |
Family ID | 1000004368045 |
Filed Date | 2021-03-18 |
United States Patent
Application |
20210082471 |
Kind Code |
A1 |
Detroja; Parth Popatlal ; et
al. |
March 18, 2021 |
SYSTEMS AND METHODS FOR GENERATING MUSIC RECOMMENDATIONS
Abstract
Systems, methods, and non-transitory computer-readable media can
be configured to determine a video embedding for a video content
item based at least in part on a first machine learning model. A
set of music embeddings can be determined for a set of music
content items based at least in part on a second machine learning
model. The set of music content items can be ranked based at least
in part on the video embedding and the set of music embeddings.
Inventors: |
Detroja; Parth Popatlal;
(Redwood City, CA) ; Cao; Bokai; (Fremont, CA)
; Singh; Amit; (Los Altos Hills, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Facebook, Inc. |
Menlo Park |
CA |
US |
|
|
Family ID: |
1000004368045 |
Appl. No.: |
16/573802 |
Filed: |
September 17, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06K 9/00744 20130101;
G06K 9/6215 20130101; G11B 27/036 20130101; G06N 20/00
20190101 |
International
Class: |
G11B 27/036 20060101
G11B027/036; G06N 20/00 20060101 G06N020/00; G06K 9/00 20060101
G06K009/00; G06K 9/62 20060101 G06K009/62 |
Claims
1. A computer-implemented method comprising: generating, by a
computing system, a video embedding for a video content item based
at least in part on a first machine learning model; generating, by
the computing system, a set of music embeddings for a set of music
content items based at least in part on a second machine learning
model, wherein a music embedding of the set of music embeddings is
generated based at least in part on a combination of music feature
embeddings associated with a corresponding music content item of
the set of music content items and one or more values are removed
from the combination of music feature embeddings based at least in
part on the second machine learning model; and ranking, by the
computing system, the set of music content items based at least in
part on the video embedding and the set of music embeddings.
2. The computer-implemented method of claim 1, further comprising:
generating one or more video feature embeddings based at least in
part on one or more video features associated with the video
content item; and wherein the video embedding is generated based at
least in part on the one or more video feature embeddings.
3. The computer-implemented method of claim 2, wherein the one or
more video features associated with the video content item includes
at least one of: a concept, an object, or a visual characteristic
identified in the video content item.
4. The computer-implemented method of claim 1, further comprising:
generating the music feature embeddings for the corresponding music
content item based at least in part on music features associated
with the corresponding music content item; and wherein the
combination of the music feature embeddings is based at least in
part on a concatenation of the music feature embeddings.
5. The computer-implemented method of claim 4, wherein the music
features associated with the corresponding music content item
include at least one of: a title, an artist, a lyric, a genre, or a
spectrogram associated with the corresponding music content
item.
6. The computer-implemented method of claim 1, wherein the ranking
the set of music content items comprises: generating a subset of
music embeddings based at least in part on a proximity between the
video embedding and the set of music embeddings.
7. The computer-implemented method of claim 6, wherein the ranking
the set of music content items further comprises: ranking a subset
of the set of music content items associated with the subset of
music embeddings based at least in part on a measure of similarity
between the video embedding and the subset of music embeddings.
8. The computer-implemented method of claim 1, wherein the video
embedding and the set of music embeddings are mapped in a vector
space.
9. The computer-implemented method of claim 1, wherein the first
machine learning model and the second machine learning model are
trained based at least in part on training sets of data that
include training video content items and training music content
items included in the training video content items.
10. The computer-implemented method of claim 1, further comprising:
providing one or more music recommendations based at least in part
on the ranking.
11. A system comprising: at least one processor; and a memory
storing instructions that, when executed by the at least one
processor, cause the system to perform a method comprising:
generating a video embedding for a video content item based at
least in part on a first machine learning model; generating a set
of music embeddings for a set of music content items based at least
in part on a second machine learning model, wherein a music
embedding of the set of music embeddings is generated based at
least in part on a combination of music feature embeddings
associated with a corresponding music content item of the set of
music content items and one or more values are removed from the
combination of music feature embeddings based at least in part on
the second machine learning model; and ranking the set of music
content items based at least in part on the video embedding and the
set of music embeddings.
12. The system of claim 11, further comprising: generating one or
more video feature embeddings based at least in part on one or more
video features associated with the video content item; and wherein
the video embedding is generated based at least in part on the one
or more video feature embeddings.
13. The system of claim 12, wherein the one or more video features
associated with the video content item includes at least one of: a
concept, an object, or a visual characteristic identified in the
video content item.
14. The system of claim 11, further comprising: generating the
music feature embeddings for the corresponding music content item
based at least in part on music features associated with the
corresponding music content item; and wherein the combination of
the music feature embeddings is based at least in part on a
concatenation of the music feature embeddings.
15. The system of claim 14, wherein the one or more music features
associated with the corresponding music content item include at
least one of: a title, an artist, a lyric, a genre, or a
spectrogram associated with the corresponding music content
item.
16. A non-transitory computer-readable storage medium including
instructions that, when executed by at least one processor of a
computing system, cause the computing system to perform a method
comprising: generating a video embedding for a video content item
based at least in part on a first machine learning model;
generating a set of music embeddings for a set of music content
items based at least in part on a second machine learning model,
wherein a music embedding of the set of music embeddings is
generated based at least in part on a combination of music feature
embeddings associated with a corresponding music content item of
the set of music content items and one or more values are removed
from the combination of music feature embeddings based at least in
part on the second machine learning model; and ranking the set of
music content items based at least in part on the video embedding
and the set of music embeddings.
17. The non-transitory computer-readable storage medium of claim
16, further comprising: generating one or more video feature
embeddings based at least in part on one or more video features
associated with the video content item; and wherein the video
embedding is generated based at least in part on the one or more
video feature embeddings.
18. The non-transitory computer-readable storage medium of claim
17, wherein the one or more video features associated with the
video content item includes at least one of: a concept, an object,
or a visual characteristic identified in the video content
item.
19. The non-transitory computer-readable storage medium of claim
16, further comprising: generating the music feature embeddings for
the corresponding music content item based at least in part on
music features associated with the corresponding music content
item; and wherein the combination of the music feature embeddings
is based at least in part on a concatenation of the music feature
embeddings.
20. The non-transitory computer-readable storage medium of claim
19, wherein the music features associated with the corresponding
music content item include at least one of: a title, an artist, a
lyric, a genre, or a spectrogram associated with the corresponding
music content items.
Description
FIELD OF THE INVENTION
[0001] The present technology relates to the field of machine
learning. More particularly, the present technology relates to
generating music recommendations based on machine learning
methodologies.
BACKGROUND
[0002] Today, people often utilize computing devices (or systems)
for a wide variety of purposes. For example, users can utilize
computing devices to access a social networking system (or
service). The users can utilize the computing devices to interact
with one another, share content items, and view content items via
the social networking system. For example, a user may share a
content item, such as an image, a video, an article, or a link, via
a social networking system. Other users may access the social
networking system and interact with the shared content item.
SUMMARY
[0003] Various embodiments of the present technology can include
systems, methods, and non-transitory computer readable media
configured to determine a video embedding for a video content item
based at least in part on a first machine learning model. A set of
music embeddings can be determined for a set of music content items
based at least in part on a second machine learning model. The set
of music content items can be ranked based at least in part on the
video embedding and the set of music embeddings.
[0004] In an embodiment, one or more video feature embeddings can
be generated based at least in part on one or more video features
associated with the video content item. The video embedding can be
generated based at least in part on the one or more video feature
embeddings.
[0005] In an embodiment, the one or more video features associated
with the video content item includes at least one of: a concept, an
object, or a visual characteristic identified in the video content
item.
[0006] In an embodiment, a set of music feature embeddings can be
generated based at least in part on one or more music features
associated with the set of music content items. The set of music
embeddings can be generated based at least in part on the set of
music feature embeddings.
[0007] In an embodiment, the one or more music features associated
with the set of music content items includes at least one of: a
title, a lyric, a genre, or a spectrogram associated with the set
of music content items.
[0008] In an embodiment, the ranking the set of music content items
can include generating a subset of music embeddings based at least
in part on a proximity between the video embedding and the set of
music embeddings.
[0009] In an embodiment, the ranking the set of music content items
can include ranking a subset of the set of music content items
associated with the subset of music embeddings based at least in
part on a measure of similarity between the video embedding and the
subset of music embeddings.
[0010] In an embodiment, the video embeddings and the set of music
embeddings are mapped in a vector space.
[0011] In an embodiment, the first machine learning model and the
second machine learning model are trained based at least in part on
training sets of data that include training video content items and
training music content items included in the training video content
items.
[0012] In an embodiment, one or more music recommendations can be
provided based on the ranking.
[0013] It should be appreciated that many other features,
applications, embodiments, and/or variations of the disclosed
technology will be apparent from the accompanying drawings and from
the following detailed description. Additional and/or alternative
implementations of the structures, systems, non-transitory computer
readable media, and methods described herein can be employed
without departing from the principles of the present
technology.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 illustrates an example system including a music
recommendation module, according to an embodiment of the present
technology.
[0015] FIG. 2A illustrates an example video embedding module,
according to an embodiment of the present technology.
[0016] FIG. 2B illustrates an example music embedding module,
according to an embodiment of the present technology.
[0017] FIG. 3 illustrates an example functional block diagram,
according to an embodiment of the present technology.
[0018] FIG. 4 illustrates an example method, according to an
embodiment of the present technology.
[0019] FIG. 5 illustrates an example method, according to an
embodiment of the present technology.
[0020] FIG. 6 illustrates a network diagram of an example system
including an example social networking system that can be utilized
in various scenarios, according to an embodiment of the present
technology.
[0021] FIG. 7 illustrates an example of a computer system or
computing device that can be utilized in various scenarios,
according to an embodiment of the present technology.
[0022] The figures depict various embodiments of the disclosed
technology for purposes of illustration only, wherein the figures
use like reference numerals to identify like elements. One skilled
in the art will readily recognize from the following discussion
that alternative embodiments of the structures and methods
illustrated in the figures can be employed without departing from
the principles of the present technology described herein.
DETAILED DESCRIPTION
Approaches for Generating Music Recommendations
[0023] Today, people often utilize computing devices (or systems)
for a wide variety of purposes. For example, users can utilize
computing devices to access a social networking system (or
service). The users can utilize the computing devices to interact
with one another, share content items, and view content items via
the social networking system. For example, a user may share a
content item, such as an image, a video, an article, or a link, via
a social networking system. Another user may access the social
networking system and interact with the shared content item.
[0024] Under conventional approaches, a user can access a variety
of content items, such as images, videos, articles, and links,
provided by a social networking system (or service). Some of these
content items are shared content items that are shared by users of
the social networking system. For example, a user may access a
social networking system and interact with various content items
via the social networking system. Many of these content items may
be shared by other users of the social networking system. The user
can also share a content item via the social networking system, and
the other users can interact with the content items shared by the
user. In some cases, a user may wish to share a video content item
and enhance the video content item by including music in the video
content item. However, conventional approaches fail to provide
complementary music based on a video content item. Accordingly,
conventional approaches are ineffective in addressing these and
other problems arising in computer technology.
[0025] An improved approach rooted in computer technology overcomes
the foregoing and other disadvantages associated with conventional
approaches specifically arising in the realm of computer
technology. In various embodiments, the present technology provides
for generating a recommendation for music content items based on a
video content item and providing the recommendation to a user. The
user can provide the video content item, for example, by sharing
the video content item via a social networking system. Video
feature embeddings can be generated using machine learning
methodologies based on various features associated with the video
content item. In general, an embedding can be a numerical
representation (e.g., vector) of a feature or a set of features.
One video feature embedding can be generated, for example, based on
concepts identified in the video content item, and another video
feature embedding can be generated, for example, based on visual
characteristics identified in the video content item. Such video
feature embeddings can, using machine learning methodologies, be
combined (e.g., concatenated) and transformed (e.g., weighted,
normalized), to generate a video embedding associated with the
video content item. The video embedding can be evaluated with music
embeddings associated with music content items. The music
embeddings can be generated using machine learning methodologies to
combine and transform music feature embeddings associated with the
music content items. The music feature embeddings can be generated
using machine learning methodologies based on various features
associated with the music content items. The music feature
embeddings can be generated, for example, based on genres, titles,
artists, lyrics, and spectrograms associated with the music content
items. The music content items can be ranked based on the
evaluation of the music embeddings and the video embeddings. Higher
ranked music content items can be provided to the user as a
recommendation for the music content items. The user can choose to
enhance the video content item by including one of the music
content items. More details relating to the disclosed technology
are provided below.
[0026] FIG. 1 illustrates an example system 100 including a music
recommendation module 102, according to an embodiment of the
present technology. As shown in the example of FIG. 1, the music
recommendation module 102 can include a video embedding module 104,
a music embedding module 106, and a ranking module 108. In some
instances, the example system 100 can include at least one data
store 150. The components (e.g., modules, elements, etc.) shown in
this figure and all figures herein are exemplary only, and other
implementations may include additional, fewer, integrated, or
different components. Some components may not be shown so as not to
obscure relevant details. In various embodiments, one or more of
the functionalities described in connection with the music
recommendation module 102 can be implemented in any suitable
combinations.
[0027] In some embodiments, the music recommendation module 102 can
be implemented, in part or in whole, as software, hardware, or any
combination thereof. In general, a module as discussed herein can
be associated with software, hardware, or any combination thereof.
In some implementations, one or more functions, tasks, and/or
operations of modules can be carried out or performed by software
routines, software processes, hardware, and/or any combination
thereof. In some instances, the music recommendation module 102 can
be, in part or in whole, implemented as software running on one or
more computing devices or systems, such as on a server system or a
client computing device. In some instances, the music
recommendation module 102 can be, in part or in whole, implemented
within or configured to operate in conjunction with or be
integrated with a social networking system (or service), such as a
social networking system 630 of FIG. 6. Likewise, in some
instances, the music recommendation module 102 can be, in part or
in whole, implemented within or configured to operate in
conjunction with or be integrated with a client computing device,
such as the user device 610 of FIG. 6. For example, the music
recommendation module 102 can be implemented as or within a
dedicated application (e.g., app), a program, or an applet running
on a user computing device or client computing system. The
application incorporating or implementing instructions for
performing functionality of the music recommendation module 102 can
be created by a developer. The application can be provided to or
maintained in a repository. In some instances, the application can
be uploaded or otherwise transmitted over a network (e.g.,
Internet) to the repository. For example, a computing system (e.g.,
server) associated with or under control of the developer of the
application can provide or transmit the application to the
repository. The repository can include, for example, an "app" store
in which the application can be maintained for access or download
by a user. In response to a command by the user to download the
application, the application can be provided or otherwise
transmitted over a network from the repository to a computing
device associated with the user. For example, a computing system
(e.g., server) associated with or under control of an administrator
of the repository can cause or permit the application to be
transmitted to the computing device of the user so that the user
can install and run the application. The developer of the
application and the administrator of the repository can be
different entities in some cases, but can be the same entity in
other cases. It should be understood that many variations are
possible.
[0028] The music recommendation module 102 can be configured to
communicate and/or operate with the at least one data store 150, as
shown in the example system 100. The at least one data store 150
can be configured to store and maintain various types of data. In
some implementations, the at least one data store 150 can store
information associated with the social networking system (e.g., the
social networking system 630 of FIG. 6). The information associated
with the social networking system can include data about users,
user identifiers, social connections, social interactions, profile
information, demographic information, locations, geo-fenced areas,
maps, places, events, pages, groups, posts, communications,
content, feeds, account settings, privacy settings, a social graph,
and various other types of data. In some embodiments, the at least
one data store 150 can store information that is utilized by the
music recommendation module 102. For example, the at least one data
store 150 can store information associated with video embeddings
and music embeddings. It is contemplated that there can be many
variations or other possibilities.
[0029] In various embodiments, the video embedding module 104 can
generate a video embedding based on one or more video feature
embeddings associated with various features of a video content
item. The video feature embeddings can be generated based on the
various features of the video content item using machine learning
methodologies (e.g., image recognition). The various features can
include, for example, concepts, objects, and visual characteristics
associated with the video content item. The video embedding can be
generated based on the video feature embeddings using machine
learning methodologies. A machine learning model (e.g., multilayer
perceptron) can be trained to combine and transform the video
feature embeddings to generate the video embedding. In some cases,
a video embedding can be evaluated with music embeddings associated
with music content items, and the music content items can be ranked
based on the evaluation. A recommendation for a music content item
to be included in a video content item associated with the video
embedding can be generated based on the ranking. More details
regarding the video embedding module 104 will be provided with
reference to FIG. 2A.
[0030] In various embodiments, the music embedding module 106 can
generate a music embedding based on one or more music feature
embeddings associated with various features of a music content
item. The music feature embeddings can be generated based on the
various features of the music content item using machine learning
methodologies (e.g., text recognition, sound recognition). The
various features can include, for example, genres, titles, artists,
lyrics, and spectrograms associated with the music content item.
The music embedding can be generated based on the music feature
embeddings using machine learning methodologies. A machine learning
model (e.g., multilayer perceptron) can be trained to combine and
transform the music embeddings to generate the music embedding. In
some cases, a set of music embeddings associated with a set of
music content items can be evaluated with a video embedding, and
the set of music content items can be ranked based on the
evaluation. A recommendation for a music content item to be
included in a video content item associated with the video
embedding can be generated based on the ranking. More details
regarding the music embedding module 106 will be provided with
reference to FIG. 2B.
[0031] In various embodiments, the ranking module 108 can rank a
set of music content items based on a set of music embeddings
associated with the set of music content items and a video
embedding associated with a video content item. A set of music
embeddings can be mapped to a vector space with a video embedding.
In the vector space, proximities between the set of music
embeddings and the video embeddings can indicate various
interrelationships between the associated set of music content
items and the video content item. Music embeddings that are closer
in proximity to the video embedding in the vector space can be
associated with music content items that are more likely to be
complementary to (e.g., relatively better matched with) the video
content item than music content items associated with music
embeddings that are farther in proximity. A subset of the music
embeddings can be determined based on a nearest neighbor algorithm
(e.g., k-nearest neighbors (k-NN)). The subset of music embeddings
can include music embeddings that are within a threshold proximity
to the video embedding in the vector space. The music content items
associated with this subset of music embeddings can be potential
recommendations for inclusion in the video content item. The
ranking module 108 can rank a subset of music embeddings and a
recommendation for a music content item to be included in a video
content item can be generated based on the ranking. The subset of
music embeddings can be ranked based on an evaluation of the subset
of music embeddings and a video content item associated with the
video content item. The evaluation can, for example, be based on a
proximity or a measure of similarity (e.g., cosine similarity)
between the subset of music embeddings and the video embedding.
Higher ranked music embeddings in the subset of music embeddings
can be associated with music content items that are more
complementary to the video content item than music content items in
the subset of music embeddings that are lower ranked. A
recommendation for a music content item to be included in the video
content item can be generated based on the music content item being
associated with the highest ranked music embedding in the subset of
music embeddings. In some cases, recommendations for music content
items to be included in the video content item can be generated
based on the music content items being associated with music
embeddings in the subset of music embeddings that satisfy a
threshold ranking. For example, a user can provide a video content
item, and a video embedding can be generated based on the video
content item. The video embedding can be mapped in a vector space
with a set of music embeddings associated with a library of
available music content items. A subset of music embeddings can be
determined based on music embeddings in the set of music embeddings
that are within a threshold proximity of the video embedding. The
subset of music embeddings can be evaluated with the video
embedding and ranked based on a measure of similarity to the video
embedding. The highest ranked music embedding can be associated
with a music content item that is more likely to complement the
video content item. A recommendation to include the music content
item in the video content item can be generated and provided to the
user. Many variations are possible.
[0032] FIG. 2A illustrates an example video embedding module 202
configured to generate a video embedding based on a video content
item, according to an embodiment of the present technology. In some
embodiments, the video embedding module 104 of FIG. 1 can be
implemented as the video embedding module 202. As shown in FIG. 2A,
the video embedding module 202 can include a video feature
embedding module 204 and a video embedding generation module
206.
[0033] The video feature embedding module 204 can generate video
feature embeddings based on features associated with a video
content item. Features associated with a video content item can
include, for example, concepts, objects, and visual characteristics
identified in the video content item. The features can be
identified based on machine learning methodologies applied to one
or more frames of the video content item. A concept identified in a
video content item can describe an idea or impression associated
with the video content item. For example, a concept can be an
event, such as a birthday, a wedding, or a festival. A concept can
be a time of day, such as morning, noon, or night. A concept can be
a type of scenery, such as a nature scene, a sunset, a beach scene,
or a city scene. A concept can be a location, such as a forest, an
ocean, a beach, or a city. An object identified in a video content
item can describe one or more items depicted by the video content
item. For example, an object can be a face, a building, a vehicle,
or a shape. A visual characteristic identified in a video content
item can be a visual quality or visual trait associated with the
video content item. For example, a visual characteristic can be a
color scheme or an art style. Many variations are possible.
[0034] Video feature embeddings based on features associated with a
video content item can be generated based on one or more machine
learning methodologies. One or more machine learning models can be
trained to identify concepts, objects, and visual characteristics
in a video content item. The machine learning models can be applied
to a video content item to identify concepts, objects, and visual
characteristics in the video content item. The machine learning
models can be trained with training sets of data including frames
of video content items and concept labels associated with the
frames, object labels associated with the frames, or visual
characteristics labels associated with the frames. Positive
training data can include frames of video content items and concept
labels of concepts identified in the frames, object labels of
objects identified in the frames, or visual characteristics labels
of visual characteristics identified in the frames. Negative
training data can include frames of video content items and concept
labels of concepts that are not identified in the frames, object
labels of objects not identified in the frames, or visual
characteristics labels of visual characteristics not identified in
the frames. A trained machine learning model can be applied to a
video content item, or one or more frames of the video content
item, and generate a video feature embedding based on concepts,
objects, or visual characteristics identified in the video content
item. A video feature embedding can be a numerical representation
of features, such as concepts, objects, or visual characteristics,
identified in a video content item. The video feature embedding can
be mapped to a vector space and compared with other video feature
embeddings based on features identified in other video content
items. Video content items with video feature embeddings that are
closer in proximity may include features with a greater degree of
similarity than video content items with video feature embeddings
that are farther in proximity. For example, a machine learning
model can be trained using a training set of data that includes
frames of video content items and concept labels of concepts
identified in the frames of the video content items. The trained
machine learning model can be applied, for example, to frames of an
input video content item depicting a walk through a park. The
trained machine learning can generate a video feature embedding
based on the frames of the input video content item and concepts,
such as nature and park, identified in the frames of the input
video content item. The video feature embedding associated with the
input video content item can be mapped to a vector space and
compared with video feature embeddings of other video content
items. In this example, some of the other video content items may
also depict nature concepts and park concepts. The video feature
embeddings associated with other video content items that also
depict nature concepts and park concepts can be closer in proximity
to the video feature embedding associated with the input video
content item than video feature embeddings associated with other
video content items that do not depict nature concepts and park
concepts. Based on the proximities of the video feature embedding
associated with the input video content item to the other video
feature embeddings of the other video content items, it can be
determined that the other video content items that depict nature
concepts and park concepts are more similar to the input video
content item than the other video content items that do not depict
nature concepts and park concepts. Many variations are
possible.
[0035] In some cases, a machine learning model (e.g., convolutional
neural network, deep neural network) can be utilized to generate
different types of video feature embeddings, including video
feature embeddings based on concepts identified in a video content
item, video feature embeddings based on objects identified in the
video content item, and video feature embeddings based on visual
characteristics identified in the video content item. The machine
learning model can include multiple layers, and the layers can
correspond to different features that can be identified in a frame
of a video content item. Low-level layers of the machine learning
model can correspond to, for example, edges or other semantic
information identified in the frame. High-level layers of the
machine learning model can connect information from the low-level
stages to identify, for example, concepts, objects, or visual
characteristics in the frame. Video feature embeddings can be
generated based on different layers of the machine learning model.
For example, a first layer in a machine learning model can output a
video feature embedding based on concepts identified in a video
content item. A second layer, which can be a lower-level layer than
the first layer, in the machine learning model can output a video
feature embedding based on objects identified in the video content
item. A third layer, which can be a lower-level layer than the
first layer and the second layer, can output a video feature
embedding based on visual characteristics identified in the video
content item. Many variations are possible.
[0036] The video embedding generation module 206 can generate a
video embedding associated with a video content item based on one
or more video feature embeddings associated with the video content
item. The video feature embeddings can be combined (e.g.,
concatenated). The combination of video feature embeddings can be
transformed (e.g. weighted, normalized). The transformation can
involve weighting values of the combination of video feature
embeddings, normalizing values of the combination of video feature
embeddings, and removing values from the combination of video
feature embeddings. In some cases, the transformation can involve
applying a sigmoidal function to the combination of video feature
embeddings. The transformed combination of video feature embeddings
can be utilized as the video embedding. The video embedding can be
evaluated with music embeddings associated with music content
items, and the music content items can be ranked based on the
evaluation. A video embedding can be generated based on one or more
machine learning methodologies. A machine learning model can be
trained to combine and transform video feature embeddings to
generate a video embedding. The machine learning model can be
applied to one or more video feature embeddings to generate a video
embedding. The machine learning model can be trained with training
sets of data including video feature embeddings associated with
video content items and music content items associated with the
video content items. Positive training data can include video
feature embeddings associated with video content items and music
content items included in the video content items. Negative
training data can include video feature embeddings associated with
video content items and music content items not included in the
video content items. A trained machine learning model can be
applied to video feature embeddings associated with a video content
item, and the trained machine learning model can combine the video
feature embeddings and transform the combination of video feature
embeddings to generate a video embedding. A video embedding can be
a numerical representation of features associated with a video
content item. The video embedding can be mapped to a vector space
and compared with music embeddings associated with music content
items. Music content items associated with music embeddings that
are closer in proximity to the video embedding can be ranked higher
than music content items associated with music embeddings that are
farther in proximity to the video embedding. Many variations are
possible.
[0037] FIG. 2B illustrates an example music embedding module 252
configured to generate a music embedding based on a music content
item, according to an embodiment of the present technology. In some
embodiments, the music embedding module 106 of FIG. 1 can be
implemented as the music embedding module 252. As shown in FIG. 2B,
the music embedding module 252 can include a music feature
embedding module 254 and a music embedding generation module
256.
[0038] The music feature embedding module 254 can generate music
feature embeddings based on features associated with a music
content item. Features associated with a music content item can
include, for example, a song title, an artist, genres, lyrics, and
one or more spectrograms associated with the music content item. A
music feature embedding can be generated based on machine learning
methodologies (e.g., text recognition, sound recognition). One or
more machine learning models can be trained to generate music
feature embeddings based on features associated with music content
items. The machine learning models can be trained with training
sets of data including music content items and associated song
titles, artists, genres, lyrics, or spectrograms. Positive training
data can include music content items with similar song titles,
similar artists, similar genres, or similar lyrics. Positive
training data can also include spectrograms associated with similar
music content items. Negative training data can include music
content items with dissimilar song titles, dissimilar artists,
dissimilar genres, or dissimilar lyrics. Negative training data can
also include spectrograms associated with dissimilar music content
items. A trained machine learning model can be applied to a music
content item and generate a music feature embedding based on,
individually or in combination, a song title, an artist, genres,
lyrics, or spectrograms associated with the music content item. The
music feature embedding can be mapped to a vector space and
compared with other music feature embeddings based on features
associated with other music content items. Music content items
corresponding to music feature embeddings that are closer in
proximity may be associated with features that are more similar
than music content items corresponding to music embeddings that are
farther in proximity. For example, a machine learning model can be
trained using a training set of data that includes spectrograms
associated with music content items. The trained machine learning
model can be applied to a spectrogram of an input music content
item and generate a music feature embedding corresponding to the
input music content item. The music feature embedding can be mapped
to a vector space with music feature embeddings corresponding to
other music content items. Music content items corresponding to
music feature embeddings that are closer in proximity to the music
feature embedding corresponding to the input music content item can
be associated with spectrograms that are more similar to the
spectrogram associated with the input music content item than music
content items corresponding to music feature embeddings that are
farther in proximity to the music feature embedding corresponding
to the input music content item. In some cases, the music content
items that are associated with spectrograms that are more similar
to the spectrogram associated with the input music content item can
be considered to sound more similar to the input music content item
than the music content items that are associated with spectrograms
that are less similar to the spectrogram associated with the input
music content item. Many variations are possible.
[0039] The music embedding generation module 256 can generate a
music embedding associated with a music content item based on one
or more music feature embeddings associated with the music content
item. The music feature embeddings can be combined (e.g.,
concatenated). The combination of music feature embeddings can be
transformed (e.g., weighted, normalized). The transformation can
involve weighting values of the combination of music feature
embeddings, normalizing values of the combination of music feature
embeddings, and removing values from the combination of music
feature embeddings. In some cases, the transformation can involve
applying a sigmoidal function to the combination of music feature
embeddings. The transformed combination of music feature embeddings
can be utilized as the music embedding. Music embeddings associated
with music content items can be evaluated with a video embedding
associated with a video content item, and the music content items
can be ranked based on the evaluation. A music embedding can be
generated based on one or more machine learning methodologies. A
machine learning model can be trained to combine and transform
music feature embeddings to generate a music embedding. The machine
learning model can be applied to one or more music feature
embeddings to generate a music embedding. The machine learning
model can be trained with training sets of data including music
feature embeddings associated with music content items and video
content items associated with the music content items. Positive
training data can include music feature embeddings associated with
music content items and video content items in which the music
content items were included. Negative training data can include
music feature embeddings associated with music content items and
video content items in which the music content items were not
included. A trained machine learning model can be applied to music
feature embeddings associated with a music content item, and the
trained machine learning model can combine the music feature
embeddings and transform the combination of music feature
embeddings to generate a music embedding. A music embedding can be
a numerical representation of features associated with a music
content item. Music embeddings associated with music content items
can be mapped to a vector space and compared with a video embedding
associated with a video content item. The music content items can
be ranked based on the comparison of the music embeddings with the
video embedding. Many variations are possible.
[0040] FIG. 3 illustrates an example functional block diagram 300,
according to an embodiment of the present technology. The example
functional block diagram 300 illustrates an example usage of
machine learning methodologies for generating music
recommendations, as can be performed by the music recommendation
module 102 of FIG. 1. It should be understood that there can be
additional, fewer, or alternative steps performed in similar or
alternative orders, or in parallel, based on the various features
and embodiments discussed herein unless otherwise stated.
[0041] In this example, a video content item 302 can be provided to
a video feature embedding model 304. The video feature embedding
model 304 can generate a video object embedding 306a and a video
concept embedding 306b based on objects identified in the video
content item 302 and concepts identified in the video content item
302. The video object embedding 306a and the video concept
embedding 306b can be provided to a video embedding generation
model 308. The video embedding generation model 308 can combine and
transform the video object embedding 306a and the video concept
embedding 306b to generate a video embedding 310. Also in this
example, a set of music content items 312 can be provided to music
feature embedding models 314. The music feature embedding models
314 can generate a set of music title and lyric embeddings 316a, a
set of music genre embeddings 316b, and a set of music spectrogram
embeddings 316c based on titles and lyrics identified in the set of
music content items 312, genres identified in the set of music
content items 312, and spectrograms associated with the set of
music content items 312. Each music content item in the set of
music content items 312 can correspond to a respective music title
and lyric embedding, a respective music genre embedding, and a
respective music spectrogram embedding. The set of music title and
lyric embeddings 316a, the set of music genre embeddings 316b, and
the set of music spectrogram embeddings 316c can be provided to a
music embedding generation model 318. The music embedding
generation model 318 can combine and transform the set of music
title and lyric embeddings 316a, the set of music genre embeddings
316b, and the set of music spectrogram embeddings 316c to generate
a set of music embeddings 320. Each music content item in the set
of music content items 312 can have a corresponding music embedding
in the set of music embeddings 320. Further in this example, the
video embedding 310 and the set of music embeddings 320 can be
utilized in performing an embedding evaluation 322. The embedding
evaluation 322 can involve mapping the video embedding 310 and the
music embeddings 322 to a vector space, identifying a subset of
music embeddings from the set of music embeddings 320 based on a
proximity between each music embedding in the set of music
embeddings 320 and the video embedding 310 and ranking the subset
of music embeddings based on a cosine similarity between each music
embedding in the subset of music embeddings and the video embedding
310. Based on the embedding evaluation 322, music recommendations
324 can be generated for the video content item 302. All examples
herein are provided for illustrative purposes, and there can be
many variations and other possibilities.
[0042] FIG. 4 illustrates an example method 400, according to an
embodiment of the present technology. It should be understood that
there can be additional, fewer, or alternative steps performed in
similar or alternative orders, or in parallel, based on the various
features and embodiments discussed herein unless otherwise
stated.
[0043] At block 402, the example method 400 generates a video
embedding for a video content item based at least in part on a
first machine learning model. At block 404, the example method 400
generates a set of music embeddings for a set of music content
items based at least in part on a second machine learning model. At
block 406, the example method 400 ranks the set of music content
items based at least in part on the video embedding and the set of
music embeddings.
[0044] FIG. 5 illustrates an example method 500, according to an
embodiment of the present technology. It should be understood that
there can be additional, fewer, or alternative steps performed in
similar or alternative orders, or in parallel, based on the various
features and embodiments discussed herein unless otherwise
stated.
[0045] At block 502, the example method 500 generates one or more
video feature embeddings based at least in part on one or more
video features associated with a video content item. At block 504,
the example method 500 generates a video embedding for the video
content item based at least in part on the one or more video
feature embeddings and a first machine learning model. At block
506, the example method 500 generates a set of music feature
embeddings based at least in part on one or more music features
associated with a set of music content items. At block 508, the
example method 500 generates a set of music embeddings for the set
of music content items based at least in part on the set of music
feature embeddings and a second machine learning model. At block
510, the example method 500 generates a subset of music embeddings
based at least in part on a proximity between the video embedding
and the set of music embeddings. At block 512, the example method
500 ranks a subset of the set of music content items associated
with the subset of music embeddings based at least in part on a
measure of similarity between the video embedding and the subset of
music embeddings.
[0046] It is contemplated that there can be many other uses,
applications, and/or variations associated with the various
embodiments of the present technology. For example, in some cases,
a user can choose whether or not to opt-in to utilize the present
technology. The present technology can also ensure that various
privacy settings and preferences are maintained and can prevent
private information from being divulged. In another example,
various embodiments of the present technology can learn, improve,
and/or be refined over time.
Social Networking System--Example Implementation
[0047] FIG. 6 illustrates a network diagram of an example system
600 that can be utilized in various scenarios, according to an
embodiment of the present technology. The system 600 includes one
or more user devices 610, one or more external systems 620, a
social networking system (or service) 630, and a network 650. In an
embodiment, the social networking service, provider, and/or system
discussed in connection with the embodiments described above may be
implemented as the social networking system 630. For purposes of
illustration, the embodiment of the system 600, shown by FIG. 6,
includes a single external system 620 and a single user device 610.
However, in other embodiments, the system 600 may include more user
devices 610 and/or more external systems 620. In certain
embodiments, the social networking system 630 is operated by a
social network provider, whereas the external systems 620 are
separate from the social networking system 630 in that they may be
operated by different entities. In various embodiments, however,
the social networking system 630 and the external systems 620
operate in conjunction to provide social networking services to
users (or members) of the social networking system 630. In this
sense, the social networking system 630 provides a platform or
backbone, which other systems, such as external systems 620, may
use to provide social networking services and functionalities to
users across the Internet.
[0048] The user device 610 comprises one or more computing devices
that can receive input from a user and transmit and receive data
via the network 650. In one embodiment, the user device 610 is a
conventional computer system executing, for example, a Microsoft
Windows compatible operating system (OS), Apple OS X, and/or a
Linux distribution. In another embodiment, the user device 610 can
be a device having computer functionality, such as a smart-phone, a
tablet, a personal digital assistant (PDA), a mobile telephone,
etc. The user device 610 is configured to communicate via the
network 650. The user device 610 can execute an application, for
example, a browser application that allows a user of the user
device 610 to interact with the social networking system 630. In
another embodiment, the user device 610 interacts with the social
networking system 630 through an application programming interface
(API) provided by the native operating system of the user device
610, such as iOS and ANDROID. The user device 610 is configured to
communicate with the external system 620 and the social networking
system 630 via the network 650, which may comprise any combination
of local area and/or wide area networks, using wired and/or
wireless communication systems.
[0049] In one embodiment, the network 650 uses standard
communications technologies and protocols. Thus, the network 650
can include links using technologies such as Ethernet, 802.11,
worldwide interoperability for microwave access (WiMAX), 3G, 4G,
CDMA, GSM, LTE, digital subscriber line (DSL), etc. Similarly, the
networking protocols used on the network 650 can include
multiprotocol label switching (MPLS), transmission control
protocol/Internet protocol (TCP/IP), User Datagram Protocol (UDP),
hypertext transport protocol (HTTP), simple mail transfer protocol
(SMTP), file transfer protocol (FTP), and the like. The data
exchanged over the network 650 can be represented using
technologies and/or formats including hypertext markup language
(HTML) and extensible markup language (XML). In addition, all or
some links can be encrypted using conventional encryption
technologies such as secure sockets layer (SSL), transport layer
security (TLS), and Internet Protocol security (IPsec).
[0050] In one embodiment, the user device 610 may display content
from the external system 620 and/or from the social networking
system 630 by processing a markup language document 614 received
from the external system 620 and from the social networking system
630 using a browser application 612. The markup language document
614 identifies content and one or more instructions describing
formatting or presentation of the content. By executing the
instructions included in the markup language document 614, the
browser application 612 displays the identified content using the
format or presentation described by the markup language document
614. For example, the markup language document 614 includes
instructions for generating and displaying a web page having
multiple frames that include text and/or image data retrieved from
the external system 620 and the social networking system 630. In
various embodiments, the markup language document 614 comprises a
data file including extensible markup language (XML) data,
extensible hypertext markup language (XHTML) data, or other markup
language data. Additionally, the markup language document 614 may
include JavaScript Object Notation (JSON) data, JSON with padding
(JSONP), and JavaScript data to facilitate data-interchange between
the external system 620 and the user device 610. The browser
application 612 on the user device 610 may use a JavaScript
compiler to decode the markup language document 614.
[0051] The markup language document 614 may also include, or link
to, applications or application frameworks such as FLASH.TM. or
Unity.TM. applications, the SilverLight.TM. application framework,
etc.
[0052] In one embodiment, the user device 610 also includes one or
more cookies 616 including data indicating whether a user of the
user device 610 is logged into the social networking system 630,
which may enable modification of the data communicated from the
social networking system 630 to the user device 610.
[0053] The external system 620 includes one or more web servers
that include one or more web pages 622a, 622b, which are
communicated to the user device 610 using the network 650. The
external system 620 is separate from the social networking system
630. For example, the external system 620 is associated with a
first domain, while the social networking system 630 is associated
with a separate social networking domain. Web pages 622a, 622b,
included in the external system 620, comprise markup language
documents 614 identifying content and including instructions
specifying formatting or presentation of the identified
content.
[0054] The social networking system 630 includes one or more
computing devices for a social network, including a plurality of
users, and providing users of the social network with the ability
to communicate and interact with other users of the social network.
In some instances, the social network can be represented by a
graph, i.e., a data structure including edges and nodes. Other data
structures can also be used to represent the social network,
including but not limited to databases, objects, classes, meta
elements, files, or any other data structure. The social networking
system 630 may be administered, managed, or controlled by an
operator. The operator of the social networking system 630 may be a
human being, an automated application, or a series of applications
for managing content, regulating policies, and collecting usage
metrics within the social networking system 630. Any type of
operator may be used.
[0055] Users may join the social networking system 630 and then add
connections to any number of other users of the social networking
system 630 to whom they desire to be connected. As used herein, the
term "friend" refers to any other user of the social networking
system 630 to whom a user has formed a connection, association, or
relationship via the social networking system 630. For example, in
an embodiment, if users in the social networking system 630 are
represented as nodes in the social graph, the term "friend" can
refer to an edge formed between and directly connecting two user
nodes.
[0056] Connections may be added explicitly by a user or may be
automatically created by the social networking system 630 based on
common characteristics of the users (e.g., users who are alumni of
the same educational institution). For example, a first user
specifically selects a particular other user to be a friend.
Connections in the social networking system 630 are usually in both
directions, but need not be, so the terms "user" and "friend"
depend on the frame of reference. Connections between users of the
social networking system 630 are usually bilateral ("two-way"), or
"mutual," but connections may also be unilateral, or "one-way." For
example, if Bob and Joe are both users of the social networking
system 630 and connected to each other, Bob and Joe are each
other's connections. If, on the other hand, Bob wishes to connect
to Joe to view data communicated to the social networking system
630 by Joe, but Joe does not wish to form a mutual connection, a
unilateral connection may be established. The connection between
users may be a direct connection; however, some embodiments of the
social networking system 630 allow the connection to be indirect
via one or more levels of connections or degrees of separation.
[0057] In addition to establishing and maintaining connections
between users and allowing interactions between users, the social
networking system 630 provides users with the ability to take
actions on various types of items supported by the social
networking system 630. These items may include groups or networks
(i.e., social networks of people, entities, and concepts) to which
users of the social networking system 630 may belong, events or
calendar entries in which a user might be interested,
computer-based applications that a user may use via the social
networking system 630, transactions that allow users to buy or sell
items via services provided by or through the social networking
system 630, and interactions with advertisements that a user may
perform on or off the social networking system 630. These are just
a few examples of the items upon which a user may act on the social
networking system 630, and many others are possible. A user may
interact with anything that is capable of being represented in the
social networking system 630 or in the external system 620,
separate from the social networking system 630, or coupled to the
social networking system 630 via the network 650.
[0058] The social networking system 630 is also capable of linking
a variety of entities. For example, the social networking system
630 enables users to interact with each other as well as external
systems 620 or other entities through an API, a web service, or
other communication channels. The social networking system 630
generates and maintains the "social graph" comprising a plurality
of nodes interconnected by a plurality of edges. Each node in the
social graph may represent an entity that can act on another node
and/or that can be acted on by another node. The social graph may
include various types of nodes. Examples of types of nodes include
users, non-person entities, content items, web pages, groups,
activities, messages, concepts, and any other things that can be
represented by an object in the social networking system 630. An
edge between two nodes in the social graph may represent a
particular kind of connection, or association, between the two
nodes, which may result from node relationships or from an action
that was performed by one of the nodes on the other node. In some
cases, the edges between nodes can be weighted. The weight of an
edge can represent an attribute associated with the edge, such as a
strength of the connection or association between nodes. Different
types of edges can be provided with different weights. For example,
an edge created when one user "likes" another user may be given one
weight, while an edge created when a user befriends another user
may be given a different weight.
[0059] As an example, when a first user identifies a second user as
a friend, an edge in the social graph is generated connecting a
node representing the first user and a second node representing the
second user. As various nodes relate or interact with each other,
the social networking system 630 modifies edges connecting the
various nodes to reflect the relationships and interactions.
[0060] The social networking system 630 also includes
user-generated content, which enhances a user's interactions with
the social networking system 630. User-generated content may
include anything a user can add, upload, send, or "post" to the
social networking system 630. For example, a user communicates
posts to the social networking system 630 from a user device 610.
Posts may include data such as status updates or other textual
data, location information, images such as photos, videos, links,
music or other similar data and/or media. Content may also be added
to the social networking system 630 by a third party. Content
"items" are represented as objects in the social networking system
630. In this way, users of the social networking system 630 are
encouraged to communicate with each other by posting text and
content items of various types of media through various
communication channels. Such communication increases the
interaction of users with each other and increases the frequency
with which users interact with the social networking system
630.
[0061] The social networking system 630 includes a web server 632,
an API request server 634, a user profile store 636, a connection
store 638, an action logger 640, an activity log 642, and an
authorization server 644. In an embodiment of the invention, the
social networking system 630 may include additional, fewer, or
different components for various applications. Other components,
such as network interfaces, security mechanisms, load balancers,
failover servers, management and network operations consoles, and
the like are not shown so as to not obscure the details of the
system.
[0062] The user profile store 636 maintains information about user
accounts, including biographic, demographic, and other types of
descriptive information, such as work experience, educational
history, hobbies or preferences, location, and the like that has
been declared by users or inferred by the social networking system
630. This information is stored in the user profile store 636 such
that each user is uniquely identified. The social networking system
630 also stores data describing one or more connections between
different users in the connection store 638. The connection
information may indicate users who have similar or common work
experience, group memberships, hobbies, or educational history.
Additionally, the social networking system 630 includes
user-defined connections between different users, allowing users to
specify their relationships with other users. For example,
user-defined connections allow users to generate relationships with
other users that parallel the users' real-life relationships, such
as friends, co-workers, partners, and so forth. Users may select
from predefined types of connections, or define their own
connection types as needed. Connections with other nodes in the
social networking system 630, such as non-person entities, buckets,
cluster centers, images, interests, pages, external systems,
concepts, and the like are also stored in the connection store
638.
[0063] The social networking system 630 maintains data about
objects with which a user may interact. To maintain this data, the
user profile store 636 and the connection store 638 store instances
of the corresponding type of objects maintained by the social
networking system 630. Each object type has information fields that
are suitable for storing information appropriate to the type of
object. For example, the user profile store 636 contains data
structures with fields suitable for describing a user's account and
information related to a user's account. When a new object of a
particular type is created, the social networking system 630
initializes a new data structure of the corresponding type, assigns
a unique object identifier to it, and begins to add data to the
object as needed. This might occur, for example, when a user
becomes a user of the social networking system 630, the social
networking system 630 generates a new instance of a user profile in
the user profile store 636, assigns a unique identifier to the user
account, and begins to populate the fields of the user account with
information provided by the user.
[0064] The connection store 638 includes data structures suitable
for describing a user's connections to other users, connections to
external systems 620 or connections to other entities. The
connection store 638 may also associate a connection type with a
user's connections, which may be used in conjunction with the
user's privacy setting to regulate access to information about the
user. In an embodiment of the invention, the user profile store 636
and the connection store 638 may be implemented as a federated
database.
[0065] Data stored in the connection store 638, the user profile
store 636, and the activity log 642 enables the social networking
system 630 to generate the social graph that uses nodes to identify
various objects and edges connecting nodes to identify
relationships between different objects. For example, if a first
user establishes a connection with a second user in the social
networking system 630, user accounts of the first user and the
second user from the user profile store 636 may act as nodes in the
social graph. The connection between the first user and the second
user stored by the connection store 638 is an edge between the
nodes associated with the first user and the second user.
Continuing this example, the second user may then send the first
user a message within the social networking system 630. The action
of sending the message, which may be stored, is another edge
between the two nodes in the social graph representing the first
user and the second user. Additionally, the message itself may be
identified and included in the social graph as another node
connected to the nodes representing the first user and the second
user.
[0066] In another example, a first user may tag a second user in an
image that is maintained by the social networking system 630 (or,
alternatively, in an image maintained by another system outside of
the social networking system 630). The image may itself be
represented as a node in the social networking system 630. This
tagging action may create edges between the first user and the
second user as well as create an edge between each of the users and
the image, which is also a node in the social graph. In yet another
example, if a user confirms attending an event, the user and the
event are nodes obtained from the user profile store 636, where the
attendance of the event is an edge between the nodes that may be
retrieved from the activity log 642. By generating and maintaining
the social graph, the social networking system 630 includes data
describing many different types of objects and the interactions and
connections among those objects, providing a rich source of
socially relevant information.
[0067] The web server 632 links the social networking system 630 to
one or more user devices 610 and/or one or more external systems
620 via the network 650. The web server 632 serves web pages, as
well as other web-related content, such as Java, JavaScript, Flash,
XML, and so forth. The web server 632 may include a mail server or
other messaging functionality for receiving and routing messages
between the social networking system 630 and one or more user
devices 610. The messages can be instant messages, queued messages
(e.g., email), text and SMS messages, or any other suitable
messaging format.
[0068] The API request server 634 allows one or more external
systems 620 and user devices 610 to call access information from
the social networking system 630 by calling one or more API
functions. The API request server 634 may also allow external
systems 620 to send information to the social networking system 630
by calling APIs. The external system 620, in one embodiment, sends
an API request to the social networking system 630 via the network
650, and the API request server 634 receives the API request. The
API request server 634 processes the request by calling an API
associated with the API request to generate an appropriate
response, which the API request server 634 communicates to the
external system 620 via the network 650. For example, responsive to
an API request, the API request server 634 collects data associated
with a user, such as the user's connections that have logged into
the external system 620, and communicates the collected data to the
external system 620. In another embodiment, the user device 610
communicates with the social networking system 630 via APIs in the
same manner as external systems 620.
[0069] The action logger 640 is capable of receiving communications
from the web server 632 about user actions on and/or off the social
networking system 630. The action logger 640 populates the activity
log 642 with information about user actions, enabling the social
networking system 630 to discover various actions taken by its
users within the social networking system 630 and outside of the
social networking system 630. Any action that a particular user
takes with respect to another node on the social networking system
630 may be associated with each user's account, through information
maintained in the activity log 642 or in a similar database or
other data repository. Examples of actions taken by a user within
the social networking system 630 that are identified and stored may
include, for example, adding a connection to another user, sending
a message to another user, reading a message from another user,
viewing content associated with another user, attending an event
posted by another user, posting an image, attempting to post an
image, or other actions interacting with another user or another
object. When a user takes an action within the social networking
system 630, the action is recorded in the activity log 642. In one
embodiment, the social networking system 630 maintains the activity
log 642 as a database of entries. When an action is taken within
the social networking system 630, an entry for the action is added
to the activity log 642. The activity log 642 may be referred to as
an action log.
[0070] Additionally, user actions may be associated with concepts
and actions that occur within an entity outside of the social
networking system 630, such as an external system 620 that is
separate from the social networking system 630. For example, the
action logger 640 may receive data describing a user's interaction
with an external system 620 from the web server 632. In this
example, the external system 620 reports a user's interaction
according to structured actions and objects in the social
graph.
[0071] Other examples of actions where a user interacts with an
external system 620 include a user expressing an interest in an
external system 620 or another entity, a user posting a comment to
the social networking system 630 that discusses an external system
620 or a web page 622a within the external system 620, a user
posting to the social networking system 630 a Uniform Resource
Locator (URL) or other identifier associated with an external
system 620, a user attending an event associated with an external
system 620, or any other action by a user that is related to an
external system 620. Thus, the activity log 642 may include actions
describing interactions between a user of the social networking
system 630 and an external system 620 that is separate from the
social networking system 630.
[0072] The authorization server 644 enforces one or more privacy
settings of the users of the social networking system 630. A
privacy setting of a user determines how particular information
associated with a user can be shared. The privacy setting comprises
the specification of particular information associated with a user
and the specification of the entity or entities with whom the
information can be shared. Examples of entities with which
information can be shared may include other users, applications,
external systems 620, or any entity that can potentially access the
information. The information that can be shared by a user comprises
user account information, such as profile photos, phone numbers
associated with the user, user's connections, actions taken by the
user such as adding a connection, changing user profile
information, and the like.
[0073] The privacy setting specification may be provided at
different levels of granularity. For example, the privacy setting
may identify specific information to be shared with other users;
the privacy setting identifies a work phone number or a specific
set of related information, such as, personal information including
profile photo, home phone number, and status. Alternatively, the
privacy setting may apply to all the information associated with
the user. The specification of the set of entities that can access
particular information can also be specified at various levels of
granularity. Various sets of entities with which information can be
shared may include, for example, all friends of the user, all
friends of friends, all applications, or all external systems 620.
One embodiment allows the specification of the set of entities to
comprise an enumeration of entities. For example, the user may
provide a list of external systems 620 that are allowed to access
certain information. Another embodiment allows the specification to
comprise a set of entities along with exceptions that are not
allowed to access the information. For example, a user may allow
all external systems 620 to access the user's work information, but
specify a list of external systems 620 that are not allowed to
access the work information. Certain embodiments call the list of
exceptions that are not allowed to access certain information a
"block list". External systems 620 belonging to a block list
specified by a user are blocked from accessing the information
specified in the privacy setting. Various combinations of
granularity of specification of information, and granularity of
specification of entities, with which information is shared are
possible. For example, all personal information may be shared with
friends whereas all work information may be shared with friends of
friends.
[0074] The authorization server 644 contains logic to determine if
certain information associated with a user can be accessed by a
user's friends, external systems 620, and/or other applications and
entities. The external system 620 may need authorization from the
authorization server 644 to access the user's more private and
sensitive information, such as the user's work phone number. Based
on the user's privacy settings, the authorization server 644
determines if another user, the external system 620, an
application, or another entity is allowed to access information
associated with the user, including information about actions taken
by the user.
[0075] In some embodiments, the social networking system 630 can
include a music surface module 646. The music surface module 646
can, for example, be implemented as the music surface module 102,
as discussed in more detail herein. As discussed previously, it
should be appreciated that there can be many variations or other
possibilities. For example, in some embodiments, one or more
functionalities of the music surface module 646 can be implemented
in the user device 610. As discussed previously, it should be
appreciated that there can be many variations or other
possibilities.
Hardware Implementation
[0076] The foregoing processes and features can be implemented by a
wide variety of machine and computer system architectures and in a
wide variety of network and computing environments. FIG. 7
illustrates an example of a computer system 700 that may be used to
implement one or more of the embodiments described herein according
to an embodiment of the invention. The computer system 700 includes
sets of instructions for causing the computer system 700 to perform
the processes and features discussed herein. The computer system
700 may be connected (e.g., networked) to other machines. In a
networked deployment, the computer system 700 may operate in the
capacity of a server machine or a client machine in a client-server
network environment, or as a peer machine in a peer-to-peer (or
distributed) network environment. In an embodiment of the
invention, the computer system 700 may be the social networking
system 630, the user device 610, and the external system 620, or a
component thereof. In an embodiment of the invention, the computer
system 700 may be one server among many that constitutes all or
part of the social networking system 630.
[0077] The computer system 700 includes a processor 702, a cache
704, and one or more executable modules and drivers, stored on a
computer-readable medium, directed to the processes and features
described herein. Additionally, the computer system 700 includes a
high performance input/output (I/O) bus 706 and a standard I/O bus
708. A host bridge 710 couples processor 702 to high performance
I/O bus 706, whereas I/O bus bridge 712 couples the two buses 706
and 708 to each other. A system memory 714 and one or more network
interfaces 716 couple to high performance I/O bus 706. The computer
system 700 may further include video memory and a display device
coupled to the video memory (not shown). Mass storage 718 and I/O
ports 720 couple to the standard I/O bus 708. The computer system
700 may optionally include a keyboard and pointing device, a
display device, or other input/output devices (not shown) coupled
to the standard I/O bus 708. Collectively, these elements are
intended to represent a broad category of computer hardware
systems, including but not limited to computer systems based on the
x86-compatible processors manufactured by Intel Corporation of
Santa Clara, Calif., and the x86-compatible processors manufactured
by Advanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as
well as any other suitable processor.
[0078] An operating system manages and controls the operation of
the computer system 700, including the input and output of data to
and from software applications (not shown). The operating system
provides an interface between the software applications being
executed on the system and the hardware components of the system.
Any suitable operating system may be used, such as the LINUX
Operating System, the Apple Macintosh Operating System, available
from Apple Computer Inc. of Cupertino, Calif., UNIX operating
systems, Microsoft.RTM. Windows.RTM. operating systems, BSD
operating systems, and the like. Other implementations are
possible.
[0079] The elements of the computer system 700 are described in
greater detail below. In particular, the network interface 716
provides communication between the computer system 700 and any of a
wide range of networks, such as an Ethernet (e.g., IEEE 802.3)
network, a backplane, etc. The mass storage 718 provides permanent
storage for the data and programming instructions to perform the
above-described processes and features implemented by the
respective computing systems identified above, whereas the system
memory 714 (e.g., DRAM) provides temporary storage for the data and
programming instructions when executed by the processor 702. The
I/O ports 720 may be one or more serial and/or parallel
communication ports that provide communication between additional
peripheral devices, which may be coupled to the computer system
700.
[0080] The computer system 700 may include a variety of system
architectures, and various components of the computer system 700
may be rearranged. For example, the cache 704 may be on-chip with
processor 702. Alternatively, the cache 704 and the processor 702
may be packed together as a "processor module", with processor 702
being referred to as the "processor core". Furthermore, certain
embodiments of the invention may neither require nor include all of
the above components. For example, peripheral devices coupled to
the standard I/O bus 708 may couple to the high performance I/O bus
706. In addition, in some embodiments, only a single bus may exist,
with the components of the computer system 700 being coupled to the
single bus. Moreover, the computer system 700 may include
additional components, such as additional processors, storage
devices, or memories.
[0081] In general, the processes and features described herein may
be implemented as part of an operating system or a specific
application, component, program, object, module, or series of
instructions referred to as "programs". For example, one or more
programs may be used to execute specific processes described
herein. The programs typically comprise one or more instructions in
various memory and storage devices in the computer system 700 that,
when read and executed by one or more processors, cause the
computer system 700 to perform operations to execute the processes
and features described herein. The processes and features described
herein may be implemented in software, firmware, hardware (e.g., an
application specific integrated circuit), or any combination
thereof.
[0082] In one implementation, the processes and features described
herein are implemented as a series of executable modules run by the
computer system 700, individually or collectively in a distributed
computing environment. The foregoing modules may be realized by
hardware, executable modules stored on a computer-readable medium
(or machine-readable medium), or a combination of both. For
example, the modules may comprise a plurality or series of
instructions to be executed by a processor in a hardware system,
such as the processor 702. Initially, the series of instructions
may be stored on a storage device, such as the mass storage 718.
However, the series of instructions can be stored on any suitable
computer readable storage medium. Furthermore, the series of
instructions need not be stored locally, and could be received from
a remote storage device, such as a server on a network, via the
network interface 716. The instructions are copied from the storage
device, such as the mass storage 718, into the system memory 714
and then accessed and executed by the processor 702. In various
implementations, a module or modules can be executed by a processor
or multiple processors in one or multiple locations, such as
multiple servers in a parallel processing environment.
[0083] Examples of computer-readable media include, but are not
limited to, recordable type media such as volatile and non-volatile
memory devices; solid state memories; floppy and other removable
disks; hard disk drives; magnetic media; optical disks (e.g.,
Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks
(DVDs)); other similar non-transitory (or transitory), tangible (or
non-tangible) storage medium; or any type of medium suitable for
storing, encoding, or carrying a series of instructions for
execution by the computer system 700 to perform any one or more of
the processes and features described herein.
[0084] For purposes of explanation, numerous specific details are
set forth in order to provide a thorough understanding of the
description. It will be apparent, however, to one skilled in the
art that embodiments of the technology can be practiced without
these specific details. In some instances, modules, structures,
processes, features, and devices are shown in block diagram form in
order to avoid obscuring the description. In other instances,
functional block diagrams and flow diagrams are shown to represent
data and logic flows. The components of block diagrams and flow
diagrams (e.g., modules, blocks, structures, devices, features,
etc.) may be variously combined, separated, removed, reordered, and
replaced in a manner other than as expressly described and depicted
herein.
[0085] Reference in this specification to "one embodiment", "an
embodiment", "other embodiments", "one series of embodiments",
"some embodiments", "various embodiments", or the like means that a
particular feature, design, structure, or characteristic described
in connection with the embodiment is included in at least one
embodiment of the present technology. The appearances of, for
example, the phrase "in one embodiment" or "in an embodiment" in
various places in the specification are not necessarily all
referring to the same embodiment, nor are separate or alternative
embodiments mutually exclusive of other embodiments. Moreover,
whether or not there is express reference to an "embodiment" or the
like, various features are described, which may be variously
combined and included in some embodiments, but also variously
omitted in other embodiments. Similarly, various features are
described that may be preferences or requirements for some
embodiments, but not other embodiments.
[0086] The language used herein has been principally selected for
readability and instructional purposes, and it may not have been
selected to delineate or circumscribe the inventive subject matter.
It is therefore intended that the scope of the invention be limited
not by this detailed description, but rather by any claims that
issue on an application based hereon. Accordingly, the disclosure
of the embodiments of the invention is intended to be illustrative,
but not limiting, of the scope of the invention, which is set forth
in the following claims.
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