METRICS-BASED ACCESSING OF SOCIAL THREADS

• US 20140214830A1
• Filed: 04/01/2014
• Published: 07/31/2014
• Est. Priority Date: 05/20/2010
• Status: Abandoned Application

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Fischer, Ronald F.
• Application Number:
  US14/242,367
• Publication Number:
  US 20140214830A1
• Time in Patent Office: Days
• US Class Current: 707/736
• CPC Class Codes: G06F 16/95 Retrieval from the web
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

A method of accessing feeds based on metrics is provided. Feeds, each associated with an object stored in a database system, are provided to users of the database system. Inferential user interaction data captures implicit user behavior of users of the database system, wherein the data is generated in relation to a feed. Feed metrics are determined based on the user interaction data, wherein a feed metric is based upon statistics comprising user consumption, user responsiveness, content proliferation, and feed life. Finally, an action is executed in relation to at least one feed based on the feed metrics, wherein the action comprises discontinuing the feed, characterizing a feed, determining that a feed can be monetized, determining that a feed should be cached, or determining that intervention in a feed is advisable.

Claims

1. A method of accessing feeds based on one or more feed metrics, the method comprising:

receiving inferential user interaction data generated in response to explicit behavior of a plurality of users in relation to a plurality of feeds, the inferential user interaction data indicating implicit user behavior of the plurality of users, the inferential user interaction data captured in response to one of client software operating on a computing device and a browser plug-in;

determining, using one or more processors of a computing device, one or more feed metrics based on statistics of the inferential user interaction data for the plurality of users;

processing the one or more feed metrics with respect to the plurality of feeds; and

altering a characteristic associated with at least one feed based on the one or more processed feed metrics.

2. The method of claim 1, wherein the statistics of the inferential user interaction data comprise at least one of:

user consumption, user responsiveness, content proliferation, and feed life.

3. The method of claim 1, wherein altering a characteristic associated with the feed comprises one or more of:

discontinuing the feed, characterizing an importance of the feed, determining that the feed can be monetized, determining that the feed is to be cached, and determining that intervention in the feed is advisable.

4. The method of claim 1, wherein altering a characteristic associated with the feed comprises selecting an action to be executed based on the one or more feed metrics.

5. The method of claim 1, wherein altering a characteristic associated with the feed comprises providing the one or more feed metrics as input to an action, and wherein the action is modified based on the one or more feed metrics.

6. The method of claim 1, wherein the implicit user behavior comprises one or more of:

hovering a cursor over a location of a page, viewing a designated area of content for a duration of time, viewing a comment on a message, selecting a text or image on a page, copying a text on a page, expanding a message from a truncated form, and increasing a view of a page.

7. The method of claim 1, wherein determining the one or more feed metrics based on statistics of the inferential user interaction data comprises determining a change in a feed metric over time.

8. The method of claim 1, wherein determining the one or more feed metrics based on statistics of the inferential user interaction data comprises determining a feed metric with respect to a subgroup of users sharing a common characteristic.

9. The method of claim 1, wherein determining the one or more feed metrics based on statistics of the inferential user interaction data comprises determining a first feed metric with respect to a second feed metric.

10. The method of claim 9, wherein the first feed metric is average user responsiveness or average user-initiated content proliferation, and the second feed metric is average user consumption.

11. The method of claim 9, wherein the first feed metric is average user consumption, average user responsiveness, or average user-initiated content proliferation, and the second feed metric is feed life.

12. The method of claim 1, wherein altering a characteristic associated with the feed comprises determining the feed as being important.

13. The method of claim 12, wherein determining the feed as being important is based on a user consumption feed metric, the user consumption feed metric indicating that the feed has a high average user consumption.

14. A non-transitory computer-readable storage medium storing instructions executable by a processor to cause a method to be performed for accessing feeds based on one or more feed metrics, the method comprising:

receiving inferential user interaction data generated in response to explicit behavior of a plurality of users in relation to a plurality of feeds, the inferential user interaction data indicating implicit user behavior of the plurality of users, the inferential user interaction data captured in response to one of client software operating on a client device and a browser plug-in;

determining, using one or more processors of a computing device, one or more feed metrics based on statistics of the inferential user interaction data for the plurality of users;

processing the one or more feed metrics with respect to the plurality of feeds; and

altering a characteristic associated with at least one feed based on the one or more processed feed metrics.

15. The non-transitory computer-readable storage medium of claim 14, wherein the statistics of the inferential user interaction data comprise at least one of user consumption, user responsiveness, content proliferation, and feed life.

16. The non-transitory computer-readable storage medium of claim 14, wherein altering a characteristic associated with the feed comprises one or more of:

discontinuing the feed, characterizing an importance of the feed, determining that the feed can be monetized, determining that the feed is to be cached, or determining that intervention in the feed is advisable.

17. The non-transitory computer-readable storage medium of claim 14, wherein the implicit user behavior comprises one or more of:

hovering a cursor over a location of a page, viewing a designated area of content for a duration of time, viewing a comment on a message, selecting a text or image on a page, copying a text on a page, expanding a message from a truncated form, and increasing a view of a page.

18. The non-transitory computer-readable storage medium of claim 14, wherein determining the one or more feed metrics based on statistics of the inferential user interaction data comprises determining the change in a feed metric over time.

19. A computing device for accessing feeds based on one or more feed metrics, the computing device comprising:

one or more processors capable of executing one or more instructions configured to cause;

receiving inferential user interaction data generated in response to explicit behavior of a plurality of users in relation to a plurality of feeds, the inferential user interaction data indicating implicit user behavior of the plurality of users, the inferential user interaction data captured in response to one of client software operating on a client device and a browser plug-in;

determining one or more feed metrics based on statistics of the inferential user interaction data for the plurality of users;

processing the one or more feed metrics with respect to the plurality of feeds; and

altering a characteristic associated with at least one feed based on the one or more processed feed metrics.

20. The computing device of claim 19, wherein the statistics of the inferential user interaction data comprise at least one of:

user consumption, user responsiveness, content proliferation, and feed life.

21. A system for accessing feeds based on one or more feed metrics, the system comprising:

a database storing a plurality of objects, wherein the database is running on one or more computing devices;

one or more processors associated with the one or more computing devices, the one or more processors capable of executing one or more instructions configured to cause;

receiving inferential user interaction data generated in response to explicit behavior of a plurality of users in relation to a plurality of feeds, the inferential user interaction data indicating implicit user behavior of the plurality of users, the inferential user interaction data captured in response to one of client software operating on a client device and a browser plug-in;

determining one or more feed metrics based on statistics of the inferential user interaction data for the plurality of users;

processing the one or more feed metrics with respect to the plurality of feeds; and

altering a characteristic associated with at least one feed based on the one or more processed feed metrics.

22. The system of claim 21, wherein the statistics of the inferential user interaction data comprise at least one of:

user consumption, user responsiveness, content proliferation, and feed life.