Social Graph Based Recommender

• US 20120001919A1
• Filed: 10/20/2009
• Published: 01/05/2012
• Est. Priority Date: 10/20/2008
• Status: Abandoned Application

• Current Assignee: Cascaad, Srl
• Original Assignee: Cascaad, Srl
• Inventors: Lumer, Erik
• Application Number:
  US13/125,298
• Publication Number:
  US 20120001919A1
• Time in Patent Office: Days
• US Class Current: 345/440
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Personalized sorted lists of data items for users within an online social network can be generated. Users within the social network are profiled based on their interests. Concepts are segmented in the ontological database into clusters of concepts that are shared by several user profiles. A social graph is defined in which nodes represent the users within the social network and edges represent the explicit connections between the users. A neighborhood graph for each concept cluster is defined. Multilayered social affinity graphs are defined. Data items acted upon by users within the social network in a given time interval are identified. Users within the social network that have acted upon the identified data items are determined. One or more layers of the social affinity graphs are selected for each identified item. Initial endorsement values in the nodes are injecting for each identified item. The endorsement values are propagated across the selected layers of the social affinity graphs for each identified item until some stopping criteria is met. A sorted list of items acted upon by other users is generated for each user within the social network.

Claims

1. A method of generating with a computing system personalized sorted lists of data items for users within an online social network, the method comprising:

defining a social graph comprising nodes and edges, wherein the nodes represent the users within the social network and users within additional social networks and the edges represent explicit connections between the users within the social network and within the additional social networks, the explicit connections including connections described on personal webpages having markup and connections described in contact lists of online communication systems;

identifying data items acted upon by users within the social network in a given time interval;

determining which of the users within the social network have acted upon the identified data items;

injecting in each node associated with such user an initial numerical endorsement value for each item acted upon by the user;

propagating the endorsement values across the social graph until some stopping criteria is met;

generating for each user within the social network a sorted list of items acted upon by other users, based on the final endorsement values accumulated for each identified item at the user'"'"'s node; and

filtering the sorted list of items by immediate relevance to the monitored context of the associated user, wherein the sorted list of items is used to adjust information being presented to the associated user, the information being selected from the group consisting of search engine results, messages received from contacts, and new entries in feeds subscribed by the associated user.

2. The method of claim 1, wherein the data items comprise one or more items uniquely referenced by a Uniform Resource Identifier (URI) from an Internet source accessed by users within the social network, the Internet source selected from the group consisting of:

1) a content site,

     2) a blog,

     3) an RSS feed,

     4) an e-commerce site, and

     5) a social message aggregator.

3. The method of claim 2, wherein the initial endorsement value attributed to an item acted upon is defined according to the actual action performed to reflect a level of interest in the item expressed explicitly or implicitly by the action.

4. The method of claim 3, wherein propagating the endorsement values across the social graph comprises:

retaining an α

portion of the initially injected endorsement value for an item at an endorsing node u and distributing the remaining (1−

α

)-portion uniformly among the nodes connecting to u;

repeating the operation for each node and for each identified item, adding to the retained portion of the endorsement value at the node the amounts that reach it by propagation; and

stopping the operation for each node and for each identified item when either the portion of accumulated endorsement value to be redistributed down the in-links of the node falls below a threshold or the node has no in-links.

5. The method of claim 4, wherein a predetermined number of items with highest final endorsement values in the sorted list at a given node is presented as recommendation to the user associated with the node.

6. The method of claim 1, wherein acting upon a data item comprises one of publishing, reading, viewing the item, listening to the item following a hyperlink featured by the item, commenting on the item and submitting a numerical rating for the item.

7. The method of claim 1, wherein the initial endorsement value attributed to an item acted upon by a user is weighted by a factor representing the user'"'"'s social authority derived from link-based analysis of the social graph structure.

8-13. -13. (canceled)

14. A method of generating with a computing system personalized sorted lists of data items for users within an online social network, the method comprising:

profiling users within the social network based on the data items they act upon or their interests, wherein the interests are derived from the automated semantic analysis of text in acted upon data items and mapping onto concepts included a general ontological database by a natural language processor;

defining a social graph comprising nodes and edges, wherein the nodes represent the users within the social network and users within additional social networks and the edges represent explicit connections between the users within the social network and within the additional social networks, the explicit connections including public connections described on personal webpages having markup;

defining a neighborhood graph, wherein nodes represent the users within the social network and edges link each user to a predefined number of other users within the social network with highest similarity in tastes or interests;

defining a social affinity graph by the union of the social graph and the neighborhood graph;

identifying data items acted upon by users within the social network in a given time interval;

determining which of the users within the social network have acted upon the identified data items;

injecting in each node of the social affinity graph corresponding to such a user an initial numerical endorsement value for each item acted upon by the user;

propagating the endorsement values across the social affinity graph until some stopping criteria is met;

generating for each user within the social network a sorted list of items acted upon by other users, based on the final endorsement values accumulated for each identified item at the user'"'"'s node, and

filtering the sorted list of items by immediate relevance to the monitored context of the associated user, wherein the sorted list of items is used to adjust information being presented to the associated user, the information being selected from the group consisting of search engine results, messages received from contacts, and new entries in feeds subscribed by the associated user.

15. The method of claim 14, wherein the data items comprise one or more items uniquely referenced through a Uniform Resource Identifier (URI) from an Internet source accessed by users within the social network, the Internet source selected from the group consisting of:

1) a content site,

     2) a blog,

     3) an RSS feed,

     4) an e-commerce site, and

     5) a social message aggregator.

16. The method of claim 15, wherein the initial endorsement value attributed to an item acted upon is defined according to the actual action performed to reflect a level of interest in the item expressed explicitly or implicitly by the action.

17. The method of claim 16, wherein the similarity in tastes or interests between two users is measured by correlating their respective user profiles.

18. The method of claim 17, wherein propagating the endorsement values across the social affinity graph comprises:

retaining an α

portion of the initially injected endorsement value for an item at an endorsing node u and distributing the remaining (1−

α

)-portion uniformly among the nodes connecting to u;

repeating the operation for each node and for each identified item, adding to the retained portion of the endorsement value at the node the amounts that reach it by propagation; and

stopping the operation for each node and for each identified item when either the portion of accumulated endorsement value to be redistributed down the in-links of the node falls below a threshold or the node has no in-links.

19. The method of claim 18, wherein a predetermined number of items with highest final endorsement values in the sorted list at a given node is presented as recommendation to the user associated with the node.

20. The method of claim 14, wherein acting upon a data item comprises one of publishing, reading, viewing the item, listening to the item, following a hyperlink featured by the item, commenting on the item and submitting a numerical rating for the item.

21. The method of claim 14, wherein the initial endorsement value attributed to an item acted upon by a user is weighted by a factor representing the user'"'"'s social authority derived from link-based analysis of the social graph structure.

25. The method of claim 14, wherein edges of the social affinity graph are weighted by at least one of the following factors:

1) profile similarity between users connected by an edge;

or

     2) degree of separation in the social graph.

26. The method of claim 25, wherein the (1−

α

)-portion of the endorsement value at any given node propagated across its in-links is distributed proportionally to their weights;

22-24. -24. (canceled)

27-29. -29. (canceled)

30. A method of generating with a computing system personalized sorted lists of data items for users within an online social network, the method comprising:

profiling users within the social network based on their interests, wherein the interests are derived from the automated semantic analysis of text in acted upon data items and mapping onto concepts included in a general ontological database by a natural language processor;

segmenting the concepts in the ontological database into clusters of concepts that are shared by several user profiles;

defining a social graph comprising nodes and edges, wherein the nodes represent the users within the social network and users within additional social networks and the edges represent explicit connections between the users within the social network and within the additional social networks, the explicit connections including connections described on personal webpages having markup and connections described in contact lists of online communication systems;

defining for each concept cluster a neighborhood graph, wherein nodes represent the users within the social network and edges link each user to a predefined number of other users within the social network with highest similarity in interests within the concept cluster;

defining multilayered social affinity graphs, wherein each layer corresponds to a different concept cluster and is formed by the union of the social graph and the neighborhood graph defined for the concept cluster;

identifying data items acted upon by users within the social network in a given time interval;

determining which of the users within the social network have acted upon the identified data items;

selecting for each identified item one or more layers of the social affinity graphs associated with the concept clusters with highest similarity to the concept mapping of the item;

injecting for each identified item initial endorsement values in the nodes corresponding to the endorsing users within the selected layers of the multilayered social affinity graphs;

propagating the endorsement values across the selected layers of the social affinity graphs for each identified item, until some stopping criteria is met;

generating for each user within the social network a sorted list of items acted upon by other users, based on the final endorsement values accumulated for each identified item at the user'"'"'s associated nodes within the multilayered social graphs; and

filtering the sorted list of items by immediate relevance to the monitored context of the associated user, wherein the sorted list of items is used to adjust information being presented to the associated user, the information being selected from the group consisting of search engine results, messages received from contacts, and new entries in feeds subscribed by the associated user.

31. The method of claim 30, wherein the data items comprise one or more items uniquely referenced by a Uniform Resource Identifier (URI) from an Internet source accessed by users within the social network, the Internet source selected from the group consisting of:

1) a content site,

     2) a blog,

     3) an RSS feed,

     4) an e-commerce site, and

     5) a social message aggregator.

32. The method of claim 31, wherein the initial endorsement value attributed to an item acted upon is defined according to the actual action performed to reflect a level of interest in the item expressed explicitly or implicitly by the action.

33. The method of claim 32, wherein the similarity in interests between two users for a given concept cluster is measured by correlating the respective components of their user profiles pertaining to the concept cluster;

34. The method of claim 33, wherein the similarity between a concept cluster and a data item is measured by the projection of the concept mapping of the item onto the concept cluster.

35. The method of claim 34, wherein the steps of propagating the endorsement values across a layer of the social affinity graphs comprises:

retaining an α

portion of the initially injected endorsement value for an item at an endorsing node u and distributing the remaining (1−

α

)-portion uniformly among the nodes connecting to u within the layer;

repeating the operation for each node and for each identified item, adding to the retained portion of the endorsement value at the node the amounts that reach it by propagation; and

stopping the operation for each node and for each identified item when either the portion of accumulated endorsement value to be redistributed down the in-links of the node falls below a threshold or the node has no in-links.

36. The method of claim 35, wherein a predetermined number of items with highest final endorsement values in the sorted list at a given node is presented as recommendation to the user associated with the node.

37. The method of claim 30, wherein acting upon a data item comprises one of publishing, reading, viewing or listening to the item as the case may be, following through a hyperlink featured by the item, commenting on the item and submitting a numerical rating for the item.

38. The method of claim 30, wherein the initial endorsement value attributed to an item acted upon by a user is weighted by a factor representing the user'"'"'s social authority derived from link-based analysis of the social graph structure.

42. The method of claim 30, wherein edges of the multilayered social affinity graphs are weighted by at least one of the following factors:

1) concept cluster-specific profile similarity between users connected by an edge;

or

     2) degree of separation in the social graph.

43. The method of claim 42, wherein the (1−

α

)-portion of the endorsement value at any given node propagated across its in-links within a given layer of the social affinity graphs is distributed proportionally to their weights;

39-41. -41. (canceled)

44-46. -46. (canceled)