Selecting social networking system user information for display via a timeline interface

• US 8,726,142 B2
• Filed: 09/21/2011
• Issued: 05/13/2014
• Est. Priority Date: 09/21/2011
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Piantino, Serkan, Case, Ryan, Funiak, Stanislav, Gibson, Daniel Klatzko, Huang, Jeff, Mack, Ryan David, McDonald, Paul M., Vijayvergiya, Arun, Wiseman, Josh, Yang, Zizhuang, Young, Steven, Yung, Raylene Kay, Zuckerberg, Mark E.
• Primary Examiner(s): Hong, Stephen
• Assistant Examiner(s): LUDWIG, MATTHEW J
• Application Number:
  US13/239,348
• Publication Number:
  US 20130073995A1
• Time in Patent Office: 965 Days
• Field of Search: 715/201, 715/203, 715/243, 715/764, 707/728, 707/734
• US Class Current: 715/203
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/447 Temporal browsing, e.g. timeline
• CPC Class Codes: G06F 16/904 Browsing; Visualisation therefor for navigating the web G06F16/954; browsing optimisation for the web G06F16/957
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

The invention provides a display interface in a social networking system that enables the presentation of information related to a user in a timeline or map view. The system accesses information about a user of a social networking system, including both data about the user and social network activities related to the user. The system then selects one or more of these pieces of data and/or activities from a certain time period and gathers them into timeline units based on their relatedness and their relevance to users. These timeline units are ranked by relevance to the user, and are used to generate a timeline or map view for the user containing visual representations of the timeline units organized by location or time. The timeline or map view is then provided to other users of the social networking system that wish to view information about the user.

Claims

1. A method comprising:

determining a time period for which information about a subject user of a social networking system is to be displayed to a viewing user via a timeline interface, where the viewing user is different from the subject user;

accessing narrative data related to the subject user;

determining the narrative data based on a relevance of the narrative data to the subject user, the narrative data associated with a time in the time period;

generating one or more timeline units from the determined narrative data;

generating a score for each of the generated timeline units, wherein each score comprises a measure of the relevance of a timeline unit to the subject user;

selecting one or more of the scored timeline units based on the scoring;

generating a timeline interface comprising displayable representations of the selected timeline units; and

sending the generated timeline interface for display to the viewing user.

2. The method of claim 1, wherein the generated timeline units have different types, and the scores for the generated timeline units are normalized across timeline units of different types.

3. The method of claim 2, wherein selecting one or more scored timeline units comprises performing a diversification process to ensure a diversity of timeline unit types.

4. The method of claim 1, wherein the score for each timeline unit is generated by a machine-learned model.

5. The method of claim 4, wherein the machine-learned model uses social data signals as input to generate a score.

6. The method of claim 5, wherein the social data signals comprise “

likes”

, user comments, user tags, user views, or user affinity.

7. The method of claim 4, wherein the machine-learned model uses at least one of image features, video features, and textual features as inputs to generate the score for the timeline units.

8. The method of claim 4, wherein user curation data collected from the timeline interface is used as training data to train the machine-learned model.

9. The method of claim 1, wherein selecting one or more scored timeline units comprises performing a de-duplication of timeline units to prevent repetitive display of narrative data.

10. The method of claim 9, wherein performing the de-duplication of timeline units comprises removing duplicated narrative data from a timeline unit based on it having a lower score than another timeline unit containing the same narrative data.

11. The method of claim 1, wherein the accessed narrative data comprises information observed by the social networking system about a plurality of actions that relate to the subject user of the social networking system, and wherein one or more of the generated timeline units comprise stories about one or more of the plurality of actions that relate to the subject user of the social networking system.

12. A method comprising:

accessing a plurality of narrative data items stored in computer memory, each item of narrative data related to the subject user and associated with a time;

for each of a plurality of time periods, selecting a set of the plurality of narrative data items that are associated with a time in the time period;

generating a plurality of timeline units from the selected set of narrative data items, each timeline unit associated with at least one time period in the plurality of time periods;

generating a score for each of the plurality of timeline units, wherein each score comprises a measure of the relevance of a timeline unit to the subject user;

determining a time period for which information is to be displayed;

selecting one or more of the scored timeline units associated with the time period to be displayed, based on the scoring;

generating a timeline interface comprising displayable representations of the selected timeline units; and

sending the generated timeline interface for display to a viewing user, where the viewing user is different from the subject user.

13. The method of claim 12, wherein the generated timeline units have different types, and the scores for the generated timeline units are normalized across timeline units of different types.

14. The method of claim 13, wherein selecting one or more scored timeline units comprises performing a diversification process to ensure a diversity of timeline unit types.

15. The method of claim 12, wherein the score for each timeline unit is generated by a machine-learned model.

16. The method of claim 15, wherein the machine-learned model uses social data signals as input to generate a score.

17. The method of claim 15, wherein the machine-learned model uses at least one of image features, video features, and textual features as inputs to generate a score.

18. The method of claim 15, wherein user curation data collected from the timeline interface is used as training data to train the machine-learned model.

19. The method of claim 12, wherein selecting one or more scored timeline units further comprises performing de-duplication of timeline units to prevent repetitive display of narrative data.

20. A method comprising:

determining a time period for which information is to be displayed;

accessing narrative data related to a subject user;

determining the narrative data most relevant to a viewing user and associated with a time in the time period, where the viewing user is different from the subject user;

generating timeline units from the most relevant narrative data associated with a time in the time period;

generating a score for each of the generated timeline units, wherein each score comprises a measure of the relevance of a timeline unit to the viewing user;

selecting one or more of the scored timeline units based on the scoring;

generating a timeline interface comprising displayable representations of the selected timeline units; and

sending the generated timeline interface for display to the viewing user.

21. The method of claim 20, wherein the generated timeline units have different types, and the scores for the generated timeline units are normalized across timeline units of different types.

22. A method comprising:

determining a time period for which information about a subject user of a social networking system is to be displayed to a viewing user via a timeline interface, where the viewing user is different from the subject user;

accessing narrative data related to the subject user;

determining the narrative data based on a relevance of the narrative data to the subject user and the viewing user, the narrative data associated with a time in the time period;

generating one or more timeline units from determined narrative data;

generating a score for each of the generated timeline units, wherein each score comprises a measure of the relevance of a timeline unit to the subject user and viewing user;

selecting one or more of the scored timeline units based on the scoring;

generating a timeline interface comprising displayable representations of the selected timeline units; and

sending the generated timeline interface for display to the viewing user.

23. The method of claim 22, wherein the generated timeline units have different types, and the scores for the generated timeline units are normalized across timeline units of different types.