Methods and systems for utilizing activity data with clustered events

• US 9,805,127 B2
• Filed: 12/10/2014
• Issued: 10/31/2017
• Est. Priority Date: 01/20/2011
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: LinkedIn Corporation (Microsoft Corporation)
• Inventors: Ruffner, Jennifer Granito, Smith, Eishay, Betz, Joseph Paul, McCarthy, Ian
• Primary Examiner(s): Nguyen, Cam-Linh
• Application Number:
  US14/565,963
• Publication Number:
  US 20150088877A1
• Time in Patent Office: 1,056 Days
• Field of Search: 707738
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9536 Search customisation based on social or collaborative filtering
• CPC Class Codes: G06F 16/9538 Presentation of query results

Abstract

The present disclosure relates to methods and systems for clustering individual items of web content, and then utilizing activity and profile data to both select clusters of content items for presentation to a user, and determining how the selected clusters of content items are presented to the user of an online social network service. With some embodiments, the activity data are derived by monitoring and detection interactions with the individual items of web content by an individual user, or other users with whom the individual user is related, as established via, and defined by, the social network service.

Claims

1. A method comprising:

by one or more processors of a set of machines, performing an analysis of a plurality of documents among which some documents each reference a common topic;

by one or more processors of the set of machines, clustering the plurality of documents by topic into a set of multiple document clusters that each correspond to a different topic and that includes a document cluster in which the documents that reference the common topic are grouped, the set of the multiple document clusters being generated by the clustering of the plurality of documents;

by one or more processors of the set of machines, detecting that a first member of a social networking service has interacted with a first document in the document cluster, the social networking service storing a profile that indicates a type of relationship between the first member and a second member of the social networking service;

by one or more processors of the set of machines, determining a rank of the document cluster relative to other document clusters in the generated set of multiple document clusters, the rank representing a likelihood that the second member is interested in the common topic of the document cluster, the determining being based on the detecting that the first member has interacted with the first document and based on the type of relationship between the first and second members; and

by one or more processors of the set of machines, presenting the second member with a reference to a second document selected from the ranked document cluster based on the determined rank of the ranked document cluster.

2. The method of claim 1, wherein:

the profile stored by the social networking service indicates that the type of relationship between the first and second members is a mutually approved connection established via the social networking service; and

the determining of the rank of the document cluster is based on the type of relationship being the mutually approved connection within the social networking service.

3. The method of claim 1, wherein:

the profile stored by the social networking service indicates that the type of relationship between the first and second members is a job title shared in common by the first and second members; and

the determining of the rank of the document cluster is based on the type of relationship being the job title shared in common.

4. The method of claim 1, wherein:

the profile stored by the social networking service indicates that the type of relationship between the first and second members is an employer shared in common by the first and second members; and

the determining of the rank of the document cluster is based on the type of relationship being the employer shared in common.

5. The method of claim 1, wherein:

the profile stored by the social networking service indicates that the type of relationship between the first and second members is a discussion group shared in common by the first and second members; and

the determining of the rank of the document cluster is based on the type of relationship being the discussion group shared in common.

6. The method of claim 1, wherein:

the common topic is a news event; and

the first and second documents are news stories regarding the same news event.

7. The method of claim 1, wherein:

the performing of the analysis of the plurality of documents performs a textual analysis of the plurality of documents to determine the different topics of the set of document clusters.

8. The method of claim 1, wherein:

the detecting that the first member has interacted with the first document in the document cluster includes detecting that the first member viewed the first document.

9. The method of claim 1, wherein:

the detecting that the first member has interacted with the first document in the document cluster includes detecting that the first member shared the first document with a third member of the social networking service.

10. The method of claim 1, wherein:

the detecting that the first member has interacted with the first document in the document cluster includes detecting that the first member recommended the first document to a third member of the social networking service.

11. The method of claim 1, wherein:

the detecting that the first member has interacted with the first document in the document cluster includes detecting that the first member submitting the first document to a third member of the social networking service via the social networking service.

12. The method of claim 1, wherein:

the detecting that the first member has interacted with the first document in the document cluster includes detecting that the first member submitted a comment that references the first document.

13. The method of claim 1, wherein:

the detecting that the first member has interacted with the first document in the document cluster includes detecting that the first member a submitted a positive rating for the first document.

14. The method of claim 1, wherein:

the determining of the rank of the document cluster includes determining a sum of sub-scores of documents within the cluster.

15. The method of claim 1, wherein:

the determining of the rank of the document cluster includes determining that biographical data of the second member includes a job title that corresponds to the common topic of the document cluster.

16. The method of claim 1, wherein:

the determining of the rank of the document cluster includes determining that biographical data of the second member includes an educational degree that corresponds to the common topic of the document cluster.

17. A non-transitory machine-readable storage medium comprising instructions that, when executed by processors of a set of machines, cause the set of machines to perform operations comprising:

by one or more processors of the set of machines, performing an analysis of a plurality of documents among which some documents each reference a common topic;

by one or more processors of the set of machines, clustering the plurality of documents by topic into a set of multiple document clusters that each correspond to a different topic and that includes a document cluster in which the documents that reference the common topic are grouped;

by one or more processors of the set of machines, detecting that a first member of a social networking service has interacted with a first document in the document cluster, the social networking service storing a profile that indicates a type of relationship between the first member and a second member of the social networking service;

by one or more processors of the set of machines, determining a rank of the document cluster relative to other document clusters in the set of multiple document clusters, the rank representing a likelihood that the second member is interested in the common topic of the document cluster, the determining being based on the detecting that the first member has interacted with the first document and based on the type of relationship between the first and second members; and

by one or more processors of the set of machines, presenting the second member with a reference to a second document selected from the ranked document cluster based on the determined rank of the ranked document cluster.

18. The non-transitory machine-readable storage medium of claim 17, wherein:

the profile stored by the social networking service indicates that the type of relationship between the first and second members is a mutually approved connection established via the social networking service; and

the determining of the rank of the document cluster is based on the type of relationship being the mutually approved connection within the social networking service.

19. A system comprising:

one or more processors; and

a memory storing instructions that, when executed by one or more of the processors, cause the one or more processors to perform operations comprising;

performing an analysis of a plurality of documents among which some documents each reference a common topic;

clustering the plurality of documents by topic into a set of multiple document clusters that each correspond to a different topic and that includes a document cluster in which the documents that reference the common topic are grouped;

detecting that a first member of a social networking service has interacted with a first document in the document cluster, the social networking service storing a profile that indicates a type of relationship between the first member and a second member of the social networking service;

determining a rank of the document cluster relative to other document clusters in the set of multiple document clusters, the rank representing a likelihood that the second member is interested in the common topic of the document cluster, the determining being based on the detecting that the first member has interacted with the first document and based on the type of relationship between the first and second members; and

presenting the second member with a reference to a second document selected from the ranked document cluster based on the determined rank of the ranked document cluster.

20. The system of claim 19, wherein:

the profile stored by the social networking service indicates that the type of relationship between the first and second members is a job title shared in common by the first and second members; and

the determining of the rank of the document cluster is based on the type of relationship being the job title shared in common.