System and method for recommending content sources

• US 8,996,529 B2
• Filed: 11/09/2011
• Issued: 03/31/2015
• Est. Priority Date: 11/16/2010
• Status: Active Grant

• Current Assignee: John Nicholas and Kristin Gross Trust U/A/D April 13 2010
• Original Assignee: John Nicholas and Kristin Gross Trust
• Inventors: Gross, John Nicholas, Albert, Philip
• Primary Examiner(s): Alam, Hosain
• Assistant Examiner(s): WITZENBURG, BRUCE A
• Application Number:
  US13/292,725
• Publication Number:
  US 20120124073A1
• Time in Patent Office: 1,238 Days
• Field of Search: 707/722, 707/739, 707/794, 715/712, 715/866
• US Class Current: 707/739
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: H04L 65/61 for supporting one-way streaming services, e.g. Internet radio
• 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

Abstract

A networked computer system identifies, optimizes and recommends content sources for users. The content sources can be used for providing news feeds, search results, etc. based on taking into net useful content contributed by such sources over other sources.

Claims

1. A method of recommending entities to a user for inclusion as content contributors for a datastream using a networked computing system comprising:

a. automatically identifying with the networked computing system an information contribution provided by each of a base set of content contributing entities selected manually by the user in a white list;

b. in response to a user request for suggestions for a first additional content contributing entity for inclusion on said white list, automatically comparing with the networked computing system content contributed by said base set of content contributing entities with content contributed by a plurality of candidate additional content contributing entities;

c. automatically recommending to said user with the networked computing system a recommendation that one or more selected ones of said plurality of candidate additional content contributing entities be included or excluded from said white list based on a measured overlap in information content between said first additional contributing entity and said one or more of said plurality of additional candidate content contributing entities;

d. in response to the user selecting one or more additional content contributing entities intended as information sources for the datastream, generating a visual display output for the user indicating graphically;

i. a first predicted change in coverage in the user'"'"'s datastream for first content associated with a first topic based on adding or removing one or more first content contributing entities; and

ii. a second predicted change in coverage in the user'"'"'s datastream for second content that is not associated with said first topic based on adding or removing said one or more first content contributing entities;

wherein the user is presented with a visual diagram of changes predicted in the user'"'"'s datastream as a result of content contributions of a user selected content contributing entity.

2. The method of claim 1, further including a step:

controlling the user'"'"'s datastream to automatically include content from said one or more selected ones of said plurality of candidate additional content contributing entities in response to the user choosing said recommendation.

3. The method of claim 1 wherein said measured overlap in information considers both a value of a common information unit contributed by both said base set of content contributing entities and plurality of candidate additional content contributing entities, as well as a timeliness factor in submitting such common information unit.

4. The method of claim 1, further including a step:

obtaining a target coverage value specified by the user for said first topic, and using said target coverage value to determine an optimal set of said selected ones of said plurality of candidate additional content contributing entities required to achieve said coverage value for said first topic within the datastream.

5. The method of claim 4, wherein information duplication is minimized for said first topic by suppressing messages that include the same content as prior messages shown within the datastream.

6. The method of claim 1, wherein the measured overlap in information content is based on measuring uniform resource locator links within electronic messages.

7. The method of claim 1, wherein said measured overlap in information content is determined over a predetermined time window.

8. The method of claim 7, wherein said predetermined time window can be selected by the user.

9. The method of claim 7, wherein information contributions outside of said time window are reduced by an exponential function.

10. The method of claim 1, further including a step:

generating a visual display output for the user, indicating graphically a predicted datastream message feed rate or predicted change in datastream message feed rate achieved based on adding or removing content contributing entities.

11. The method of claim 1, further including a step:

generating a visual display output for the user, indicating graphically a value of a data duplication rate present within the user'"'"'s datastream as calculated by the networked computing system.

17. The method of claim 1 wherein said one or more additional content contributing entities are generated by a recommendation engine and presented as a list to the user by the networked computing system.

18. The method of claim 1 wherein said visual diagram includes graphics and numerical text.

19. The method of claim 1 wherein said visual diagram includes charts and numerical text.

20. The method of claim 1 wherein said visual diagram includes an image of a set of partially overlapping individual regions, and such that each individual region corresponds to a different content contributing entity.

21. The method of claim 1 wherein only information content from said first additional contributing entity that is unique and not derived from another entity is included as part of measuring such entity'"'"'s information contribution.

12. A method of recommending entities to a user for inclusion as content contributors related to target topics in a datastream comprising:

a. receiving an indication of a first target topic and a second target topic from the user;

b. automatically identifying with the networked computing system an information contribution provided by each of a set of content contributing entities for both said first target topic and said second target topic;

c. automatically recommending to said user with the networked computing system a subset of said set of content contributing entities based on said measured information contribution for both said first target topic and said second target topic;

d. in response to the user selecting one or more additional content contributing entities intended as information sources for the datastream, generating a visual display output for said user indicating graphically;

i. a first predicted change in coverage in the datastream for first content associated with at least one of said first target topic and second target topic based on adding or removing individual content contributing entities; and

ii. a second predicted change in coverage in the datastream for second content that is not associated with at least one of said first target topic and second target topic based on adding or removing individual content contributing entities;

wherein said user is presented with a visual diagram of changes predicted in the datastream as a result of content contributions of a content contributing entity.

22. The method of claim 12 wherein only information content from said first additional contributing entity that is unique and not derived from another entity is included as part of measuring such entity'"'"'s information contribution.

23. The method of claim 12 wherein separate weightings are assigned to said first target topic and second target topic based on user input.

24. The method of claim 12 wherein separate weightings are assigned to said first target topic and second target topic based on observing user behavior.

25. The method of claim 12 wherein the networked computing system consolidates and reduces a number of entities selected by the user during step (c).

26. The method of claim 12 wherein the networked computer system computes and publishes net information gain statistics for topics to said user.

13. A method of recommending entities to a user for inclusion as content contributors related to a topic in a datastream comprising:

a. receiving an indication of a target content topic and a target topic content coverage value from the user;

b. automatically identifying with the networked computing system an information contribution value provided by each of a set of content contributing entities for said target content topic;

c. automatically generating with the networked computing system an initial subset of said set of content contributing entities which achieve said target content coverage value based on said measured information contribution;

d. configuring and adapting the user'"'"'s datastream with the computing system to include content from said initial subset of content contributing entities;

e. in response to the user selecting one or more additional content contributing entities intended as information sources for the datastream, generating a visual display output for said user indicating graphically;

i. a first predicted change in coverage in the datastream for first content associated with said target topic based on adding or removing individual content contributing entities; and

ii. a second predicted change in coverage in the datastream for second content that is not associated with said first topic based on adding or removing individual content contributing entities;

wherein said user is presented with a visual diagram identifying changes predicted to result for the user'"'"'s datastream caused by content contributions of a content contributing entity.

14. The method of claim 13, wherein the networked computing system further calculates and automatically controls the datastream to include data from a first content source having a first information contribution value and not from a second content source having a second information contribution value when an amount of content overlap calculated between the first information contribution and the second information contribution exceeds a first threshold.

15. The method of claim 14, wherein the first information contribution and the second information contribution are measured by identifying uniform resource locator links within electronic messages.

16. The method of claim 14 further including a step:

automatically selecting with the networked computing system an optimized subset of one or more of such entities from the plurality of content contribution entities for inclusion as sources to be published within the user datastream, which optimized subset is determined by computing a signal to noise ratio of said first content and said second content.

27. The method of claim 13 wherein said visual diagram includes graphics and numerical text.

28. The method of claim 13 wherein said visual diagram includes charts and numerical text.

29. The method of claim 13 wherein said visual diagram includes an image of a set of partially overlapping individual regions, and such that each individual region corresponds to a different content contributing entity.

30. The method of claim 13 wherein only information content from said first additional contributing entity that is unique and not derived from another entity is included as part of measuring such entity'"'"'s information contribution.

31. The method of claim 13 wherein step (e) occurs in response to a user request to modify said subset of content contributing entities.

32. The method of claim 13 further including a step:

automatically repeating step (d) in response to changes in said set of content contributing entities.

33. The method of claim 32 wherein step (e) occurs in response to the computing system automatically identifying new individual content contributing entities.