Generating an implied object graph based on user behavior

• US 9,904,699 B2
• Filed: 04/20/2015
• Issued: 02/27/2018
• Est. Priority Date: 09/12/2012
• Status: Active Grant

• Current Assignee: Flipboard Incorporated
• Original Assignee: Flipboard Incorporated
• Inventors: Walkingshaw, Andrew David, Aleksandrovsky, Boris Lev, van Hoff, Arthur Anthonie, Breunig, Markus
• Primary Examiner(s): LU, KUEN S
• Application Number:
  US14/691,370
• Publication Number:
  US 20150227563A1
• Time in Patent Office: 1,044 Days
• Field of Search: 707748
• CPC Class Codes: G06F 16/2237 Vectors, bitmaps or matrices
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/3326 using relevance feedback from the user, e.g. relevance feedback on documents, documents sets, document terms or passages
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 65/40 Support for services or applications

Abstract

An online system generates implied links between objects distributed by the online system based on a plurality of users'"'"' behaviors with respect to the objects. For a behavior of a user with respect to an object that satisfies a link criterion, the online system identifies a connection between the user and the object. Local object graphs are generated to represent the relationships between the objects connected to each user, and the local object graphs are merged into a global object graph. An adjacency matrix is extracted from the global object graph as a representation of the strength of relationships between the objects distributed by the online system. The online system may then apply graph analysis techniques to the adjacency matrix, thereby analyzing the relationships between the objects without relying upon explicit links between the objects.

Claims

1. A method for generating an implied object graph, the method comprising:

identifying a plurality of user behaviors by an online system, each user behavior comprising an interaction with an object of a plurality of objects and performed by a user of a plurality of users of the online system;

generating a plurality of local object graphs based on the user behaviors, each local object graph associated with a user of the plurality of users and comprising a subset of the objects with which the user interacted and links between objects of the subset, wherein a link between two objects of the subset is generated based on the user interaction with each of the two objects;

storing the links between objects of the subset of objects associated with each local object graph in a corresponding adjacency matrix;

for each local object graph, identifying at least one object that is common with another local object graphs of the plurality of local object graphs based on the links in the corresponding adjacency matrices associated with the plurality of local object graphs;

merging the plurality of local object graphs to generate a global object graph based on the identified common objects, the global object graph comprising the plurality of objects and links between the plurality of objects in the plurality of local object graphs; and

generating an adjacency matrix for the global object graph based on the adjacency matrices associated with the plurality of local object graphs.

2. The method of claim 1, further comprising:

determining a weight for each of the links in the global object graph; and

ranking the objects in the online system based on the adjacency matrix associated with the global object graph and the weights.

3. The method of claim 2, further comprising:

determining a weight for each of the links in the local object graphs, the weight determined based on the corresponding user behavior;

wherein the weight for each of the links in the global object graph is generated based on the weights of the merged local object graphs.

4. The method of claim 2, wherein ranking the objects in the online system based on the adjacency matrix associated with the global object graph and the weights comprises:

selecting one or more objects from the plurality of objects based on the adjacency matrix associated with the global object graph and the weights, each selected object having a ranking higher a threshold ranking;

determining one or more entities associated with the selected one or more objects;

identifying one or more common entities among the determined entities; and

determining one or more trends of topics among the plurality of objects based on the identified one or more common entities.

5. The method of claim 1, further comprising:

determining a weight for each of the links in the local object graphs based on a type of the corresponding user behavior.

6. The method of claim 1, further comprising:

determining a weight for each of the links in the local object graphs based on the corresponding user behavior; and

decaying the weight based on a time since the corresponding user behavior was identified.

7. The method of claim 1, wherein a user behavior of the plurality of user behaviors is a user viewing the object, and wherein generating a plurality of local object graphs comprises:

responsive to the user viewing the object, identifying a connection between the user and the object.

8. The method of claim 1, wherein generating the plurality of local objects graphs comprises:

responsive to a user of the plurality of users interacting with an object for longer than a threshold time, identifying a connection between the object and the user.

9. The method of claim 1, wherein generating the plurality of local object graphs comprises:

responsive to a user of the plurality of users sharing an object on a social networking system, identifying a connection between the object and the user.

10. The method of claim 1, wherein a user behavior of the plurality of user behaviors is an explicit feedback signal supplied by a user and corresponding to an object of the plurality of objects, and wherein generating the plurality of local object graphs comprises:

responsive to receiving the explicit feedback signal, identifying a connection between the user and the corresponding object.

11. The method of claim 1, further comprising:

calculating eigenvector centrality of the plurality of objects in the global object graph based on the adjacency matrix associated with the global object graph, the eigenvector centrality of the plurality of objects representing influence of each object in the global object graphs.

12. A non-transitory computer-readable medium storing computer program instructions for generating an implied object graph, the computer program instructions comprising instructions for:

identifying a plurality of user behaviors by an online system, each user behavior comprising an interaction with an object of a plurality of objects and performed by a user of a plurality of users of the online system;

generating a plurality of local object graphs based on the user behaviors, each local object graph associated with a user of the plurality of users and comprising a subset of the objects with which the user interacted and links between objects of the subset, wherein a link between two objects of the subset is generated based on the user interaction with each of the two objects;

storing the links between objects of the subset of objects associated with each local object graph in a corresponding adjacency matrix;

for each local object graph, identifying at least one object that is common with another local object graphs of the plurality of local object graphs based on the links in the corresponding adjacency matrices associated with the plurality of local object graphs;

merging the plurality of local object graphs to generate a global object graph based on the identified common objects, the global object graph comprising the plurality of objects and links between the plurality of objects in the plurality of local object graphs; and

generating an adjacency matrix for the global object graph based on the adjacency matrices associated with the plurality of local object graphs.

13. The non-transitory computer-readable storage medium of claim 12, the instructions further comprising instructions for:

determining a weight for each of the links in the global object graph,

ranking the objects in the online system based on the adjacency matrix associated with the global object graph and the weights.

14. The non-transitory computer-readable storage medium of claim 13, wherein ranking the objects in the online system based on the adjacency matrix associated with the global object graph and the weights comprises:

selecting one or more objects from the plurality of objects based on the adjacency matrix associated with the global object graph and the weights, each selected object having a ranking higher a threshold ranking;

determining one or more entities associated with the selected one or more objects;

identifying one or more common entities among the determined entities; and

determining one or more trends of topics among the plurality of objects based on the identified one or more common entities.

15. The non-transitory computer-readable storage medium of claim 12, the instructions further comprising instructions for:

determining a weight for each of the links in the local object graphs based on a type of the corresponding user behavior.

16. The non-transitory computer-readable storage medium of claim 12, the instructions further comprising instructions for:

determining a weight for each of the links in the local object graphs based on the corresponding user behavior; and

decaying the weight based on a time since the corresponding user behavior was identified.

17. The non-transitory computer-readable storage medium of claim 12, wherein a user behavior of the plurality of user behaviors is a user viewing the object, and wherein generating a plurality of local object graphs comprises:

responsive to the user viewing the object, identifying a connection between the user and the object.

18. The non-transitory computer-readable storage medium of claim 12, wherein generating the plurality of local objects graphs comprises:

responsive to a user of the plurality of users interacting with an object for longer than a threshold time, identifying a connection between the object and the user.

19. The non-transitory computer-readable storage medium of claim 12, wherein generating the plurality of local object graphs comprises:

responsive to a user of the plurality of users sharing an object on a social networking system, identifying a connection between the object and the user.

20. The non-transitory computer-readable storage medium of claim 12, further comprising instructions for:

calculating eigenvector centrality of the plurality of objects in the global object graph based on the adjacency matrix associated with the global object graph, the eigenvector centrality of the plurality of objects representing influence of each object in the global object graphs.