Stream of content for a channel

• US 9,723,044 B1
• Filed: 02/13/2015
• Issued: 08/01/2017
• Est. Priority Date: 12/18/2010
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Tomkins, Andrew, Rohe, Andre, Kosslyn, Justin Lewis, Dulitz, Daniel
• Primary Examiner(s): Phillips, Hassan
• Assistant Examiner(s): Rahgozar, Omeed D
• Application Number:
  US14/622,760
• Time in Patent Office: 900 Days
• Field of Search: None
• CPC Class Codes: G06F 12/06 Addressing a physical block of locations, e.g. base addressing, module addressing, memory dedication G06F12/08 takes precedence
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/435 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/438 Presentation of query results
• CPC Class Codes: G06F 16/487 using geographical or spatial information, e.g. location
• CPC Class Codes: G06F 16/489 using time information
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06N 5/00 Computing arrangements using knowledge-based models
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 65/60 Network streaming of media packets
• CPC Class Codes: H04L 65/762 at the source reformatting of additional data in video distribution servers H04N21/2355
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/60 Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources admission control or resource allocation H04L47/70

Abstract

A system and method for generating a channel includes a channel engine that retrieves candidate content items based on a topic from heterogeneous data sources. The channel engine generates a stream of content with selected content items and populates the stream of content for the channel and providing the stream of content to users associated with the channel. In response to receiving feedback, the channel engine modifies the at least one topic based at least in part on the feedback. The scoring engine generates a second stream of content from the first stream of content that is personalized for the first user based at least in part on a model. Other users can subscribe to the second stream.

Claims

1. A computer-implemented method comprising:

generating, with one or more processors, a channel for distributing content for a topic in a social network;

retrieving, with the one or more processors, candidate content items from data sources based on the topic;

determining, with the one or more processors, a reputation of a particular user who has previously interacted with the candidate content items, the reputation being based on an originality of the candidate content items, the originality of the candidate content items being based on whether a user submitted the candidate content items or transferred the candidate content items from another data source;

selecting, with the one or more processors, content items from the candidate content items based on a first item weight applied to the candidate content items and the reputation of the particular user who has previously interacted with the candidate content items;

generating, with the one or more processors, a stream of content with the selected content items;

populating, with the one or more processors, the stream of content for the channel and providing the stream of content to users associated with the channel;

receiving, with the one or more processors, feedback for the selected content items; and

modifying, with the one or more processors, the topic based on the feedback.

2. The computer-implemented method of claim 1, wherein the first item weight applied to the candidate content items is based on a relevance of the candidate content items with respect to a channel topic.

3. The computer-implemented method of claim 1, wherein the reputation of the particular user is based on a second item weight applied to a user-submitted content item submitted by the particular user.

4. The computer-implemented method of claim 1, further comprising:

determining whether a user-submitted content item was originally submitted by the particular user; and

modifying the reputation of the particular user responsive to determining that the user-submitted content item was originally submitted by the particular user.

5. The computer-implemented method of claim 1, wherein the reputation of the particular user is based on a number of the users associated with the channel and a number of interactions in the channel.

6. The computer-implemented method of claim 1, further comprising:

ranking the candidate content items based on the reputation of the particular user; and

arranging the selected content items in the stream of content according to the ranking.

7. The computer-implemented method of claim 1, further comprising:

determining whether the reputation of the particular user satisfies a threshold;

determining whether a second reputation of a moderator satisfies the threshold; and

in response to the reputation of the particular user not satisfying the threshold and the second reputation of the moderator satisfying the threshold, requesting approval by the moderator before selecting a user-submitted content item submitted by the particular user.

8. The computer-implemented method of claim 1, wherein the reputation of the particular user is based on reactions by the users associated with the channel to user-submitted content items submitted by the particular user.

9. The computer-implemented method of claim 1, wherein the first item weight applied to the candidate content items is based on a relationship between the particular user and a second user requesting the stream of content, wherein a second item weight is applied to the candidate content items if the particular user is a direct friend of the second user and a third item weight is applied to the candidate content items if the particular user is a second-generation friend of the second user.

10. A system comprising:

a processor; and

a memory storing instructions that, when executed by the processor, cause the system to;

generate a channel for distributing content for a topic in a social network;

retrieve candidate content items from data sources based on the topic;

determine a reputation of a particular user who has previously interacted with the candidate content items, the reputation being based on an originality of the candidate content items, the originality of the candidate content items being based on whether a user submitted the candidate content items or transferred the candidate content items from another data source;

select content items from the candidate content items based on a first item weight applied to the candidate content items and the reputation of the particular user who has previously interacted with the candidate content items;

generate a stream of content with the selected content items;

populate the stream of content for the channel and providing the stream of content to users associated with the channel;

receive feedback for the selected content items; and

modify the topic based on the feedback.

11. The system of claim 10, wherein the first item weight applied to the candidate content items is based on a relevance of the candidate content items with respect to a channel topic.

12. The system of claim 10, wherein the reputation of the particular user is based on a second item weight applied to a user-submitted content item submitted by the particular user.

13. The system of claim 10, wherein the instructions, when executed by the processor, also cause the system to:

determine whether a user-submitted content item was originally submitted by the particular user; and

modify the reputation of the particular user responsive to determining that the user-submitted content item was originally submitted by the particular user.

14. The system of claim 10, wherein the reputation of the particular user is based on a number of the users associated with the channel and a number of interactions in the channel.

15. The system of claim 10, wherein the instructions, when executed by the processor, also cause the system to:

rank the candidate content items based on the reputation of the particular user; and

arrange the selected content items in the stream of content according to the rank.

16. The system of claim 10, wherein the instructions, when executed by the processor, cause the system to:

determine whether the reputation of the particular user satisfies a threshold;

determine whether a second reputation of a moderator satisfies the threshold; and

in response to the reputation of the particular user not satisfying the threshold and the second reputation of the moderator satisfying the threshold, request approval by the moderator before selecting a user-submitted content item submitted by the particular user.

17. The system of claim 10, wherein the reputation of the particular user is based on reactions by the users associated with the channel to user-submitted content items submitted by the particular user.

18. The system of claim 10, wherein the first item weight applied to the candidate content items is based on a relationship between the particular user and a second user requesting the stream of content, wherein a second item weight is applied to the candidate content items if the particular user is a direct friend of the second user and a third item weight is applied to the candidate content items if the particular user is a second-generation friend of the second user.

19. A computer program product comprising a non-transitory computer usable medium including a computer readable program, wherein the computer readable program, when executed on a computer, causes the computer to:

generate a channel for distributing content for a topic in a social network;

retrieve candidate content items from data sources based on the topic;

determine a reputation of a particular user who has previously interacted with the candidate content items, the reputation being based on an originality of the candidate content items, the originality of the candidate content items being based on whether a user submitted the candidate content items or transferred the candidate content items from another data source;

select content items from the candidate content items based on a first item weight applied to the candidate content items and the reputation of the particular user who has previously interacted with the candidate content items;

generate a stream of content with the selected content items;

populate the stream of content for the channel and providing the stream of content to users associated with the channel;

receive feedback for the selected content items; and

modify the topic based on the feedback.

20. The computer program product of claim 19, wherein the first item weight applied to the candidate content items is based on a relevance of the candidate content items with respect to a channel topic.

21. The computer program product of claim 19, wherein the reputation of the particular user is based on a second item weight applied to a user-submitted content item submitted by the particular user.

22. The computer program product of claim 19, wherein the computer readable program, when executed on the computer, also causes the computer to:

determine whether a user-submitted content item was originally submitted by the particular user; and

modify the reputation of the particular user responsive to determining that the user-submitted content item was originally submitted by the particular user.

23. The computer program product of claim 19, wherein the reputation of the particular user is based on a number of the users associated with the channel and a number of interactions in the channel.

24. The computer program product of claim 19, wherein the computer readable program, when executed on the computer, also causes the computer to:

rank the candidate content items based on the reputation of the particular user; and

arrange the selected content items in the stream of content according to the rank.

25. The computer program product of claim 19, wherein the computer readable program, when executed on the computer, causes the computer to:

determine whether the reputation of the particular user satisfies a threshold;

determine whether a second reputation of a moderator satisfies the threshold; and

in response to the reputation of the particular user not satisfying the threshold and the second reputation of the moderator satisfying the threshold, request approval by the moderator before selecting a user-submitted content item submitted by the particular user.

26. The computer program product of claim 19, wherein the reputation of the particular user is based on reactions by the users associated with the channel to user-submitted content items submitted by the particular user.

27. The computer program product of claim 19, wherein the first item weight applied to the candidate content items is based on a relationship between the particular user and a second user requesting the stream of content, wherein a second item weight is applied to the candidate content items if the particular user is a direct friend of the second user and a third item weight is applied to the candidate content items if the particular user is a second-generation friend of the second user.