Bridging social silos for knowledge discovery and sharing

• US 8,793,312 B2
• Filed: 05/20/2011
• Issued: 07/29/2014
• Est. Priority Date: 05/20/2011
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Tao, Yicheng, Bochinski, Stephen Daniel, Sambhus, Sonali Mihir
• Primary Examiner(s): Shiferaw, Eleni
• Assistant Examiner(s): BROWN, DE'MARIS R
• Application Number:
  US13/112,431
• Publication Number:
  US 20120296967A1
• Time in Patent Office: 1,166 Days
• US Class Current: 709/204
• CPC Class Codes: G06Q 30/0251 Targeted advertisements

Abstract

Techniques are provided herein for obtaining network usage information about a plurality of users, analyzing the network usage information to detect a plurality of subjects and determine a set of relationship scores describing the strength of the relationship between users and subjects, and analyzing the sets of relationship scores to detect a plurality of virtual communities formed among the users. The virtual communities are used to detect subjects to recommend to members of the virtual communities, such as topics or content items that other users of the community have found of interest.

Claims

1. A method comprising:

obtaining, by a computer, network usage information about a user;

detecting, by the computer, a plurality of subjects by analyzing the network usage information, wherein the plurality of subjects comprises topics;

analyzing, by the computer, the network usage information of the user to determine a set of relationship scores, wherein each relationship score describes the strength of a relationship between the user and an individual subject in the plurality of subjects;

analyzing, by the computer, the set of relationship scores and relationship scores of other users to detect a plurality of virtual communities, wherein the user may be a member of more than one virtual community, and wherein the analyzing the set of relationship scores further comprises;

generating, by the computer, a user-topic matrix comprising the set of relationship scores for the user and the relationship scores of the other users within the virtual communities, wherein the user-topic matrix identifies one or more topics which are most strongly related with the user and each of the other users, and

applying, by the computer, a topic model to the user-topic matrix in order to generate a topic-community matrix and a user-community matrix, the topic model comprising a latent Dirichlet allocation model, wherein the topic-community matrix identifies for each topic one or more virtual communities which are most strongly associated with the topic, and the user-community matrix identifies one or more virtual communities which are most strongly associated with the user and in which the user is considered a member;

in response to a determination that the user is a member of at least one of the virtual communities, recommending, by the computer, one or more content items stored in a storage area to the user based on the virtual communities of which the user is a member, wherein said recommendation further comprises;

determining, by the computer, one or more topics that have the highest relationship scores for the user;

for each determined topic;

using the topic-community matrix to identify one or more virtual communities which are most strongly associated with the topic;

for each identified virtual community, using the user-community matrix to identify one or more other users who are most strongly associated with that virtual community;

for each identified other user, using the determined topic to search the network usage information about the identified other user in order to find content items to recommend to the user receiving a recommendation; and

in response to a determination that the user has taken an action related to the network usage information;

determining, by the computer, whether the action taken by the user is a direct action that directly relates to a content item in the storage area;

in response to a determination that the action taken is a direct action, recommending, by the computer, one or more other content items stored in the storage area to the user based at least partially upon at least one topic associated with the content item upon which the direct action is based; and

in response to a determination that the action taken is not a direct action, recommending, by the computer, one or more other content items stored in the storage area to the user based at least partially upon at least one topic associated with the action taken by the user.

2. The method of claim 1, wherein the subjects further comprise content items.

3. The method of claim 1, further comprising:

updating virtual community membership by the computer repeating said obtaining, detecting, analyzing the network usage information and analyzing the set of relationship scores steps, wherein said updating may result in changes in one or more relationship scores for the user, and the user being added or deleted as a member of one or more virtual communities.

4. The method of claim 3, wherein said updating occurs at predetermined time intervals.

5. The method of claim 1, wherein said analyzing the set of relationship scores further comprises:

applying, by the computer, a statistical correlation model to the set of relationship scores and the relationship scores of the other users in order to determine which of the other users are statistically correlated with the user; and

assigning, by the computer, the user to the same virtual community as the other users who are statistically correlated with the user.

6. The method of claim 5, wherein the statistical correlation model is a similarity model or a k-nearest neighbors model.

7. The method of claim 5, wherein said recommendation further comprises, for each virtual community of which a user is a member:

determining, by the computer, one or more subjects that have the highest relationship scores to other members of the same virtual community; and

using the determined subjects to identify one or more content items stored in the storage area and associated with the determined subjects to recommend to the user.

8. An apparatus comprising:

a memory having a plurality of content items stored therein; and

a processor configured to;

obtain network usage information about a user;

detect a plurality of subjects by analyzing the network usage information, wherein the plurality of subjects comprise topics;

analyze the network usage information of the user to determine a set of relationship scores, wherein each relationship score describes the strength of a relationship between the user and an individual subject in the plurality of subjects;

analyze the set of relationship scores and the relationship scores of other users to detect a plurality of virtual communities, wherein the user may be a member of more than one virtual community, and wherein the analyzing the set of relationship scores further comprises;

generating a user-topic matrix comprising the set of relationship scores for the user and the relationship scores of the other users within the virtual communities, wherein the user-topic matrix identifies one or more topics which are most strongly related with the user and each of the other users, and

applying a topic model to the user-topic matrix in order to generate a topic-community matrix and a user-community matrix, the topic model comprising a Dirichlet allocation model, wherein the topic-community matrix identifies for each topic one or more virtual communities which are most strongly associated with the topic, and the user-community matrix identifies one or more virtual communities which are most strongly associated with the user and in which the user is considered a member;

in response to a determination that the user is a member of at least one of the virtual communities, recommend one or more content items stored in said memory to the user based on the virtual communities of which the user is a member, wherein said recommendation further comprises the processor being further configured to;

determine one or more topics that have the highest relationship scores for the user;

for each determined topic;

use the topic-community matrix to identify one or more virtual communities which are most strongly associated with the topic;

for each identified virtual community, use the user-community matrix to identify one or more other users who are most strongly associated with that virtual community; and

for each identified other user, use the determined topic to search the network usage information about the identified other user in order to find content items to recommend to the user receiving a recommendation; and

in response to a determination that the user has taken an action related to the network usage information;

determine whether the action taken by the user is a direct action that directly relates to a content item in the storage area;

in response to a determination that the action taken is a direct action, recommend one or more other content items stored in the storage area to the user based at least partially upon at least one topic associated with the content item upon which the direct action is based; and

in response to a determination that the action taken is not a direct action, recommend one or more other content items stored in the storage area to the user based at least partially upon at least one topic associated with the action taken by the user.

9. The apparatus of claim 8, wherein said analyzing the set of relationship scores comprises the processor being further configured to:

apply a statistical correlation model to the set of relationship scores and the relationship scores of the other users in order to determine which of the other users are statistically correlated with the user, wherein the statistical correlation model is a similarity model or a k-nearest neighbors model; and

assign the user to the same virtual community as the other users who are statistically correlated with the user; and

wherein said recommendation comprises, for each virtual community of which the user is a member, the processor being further configured to;

determine one or more subjects that have the highest relationship scores to other members of the same virtual community; and

use the determined subjects to identify one or more content items associated with the determined subjects to recommend to the user.

10. One or more computer readable storage devices encoded with instructions that, when executed by a processor, cause the processor to:

obtain network usage information about a user;

detect a plurality of subjects by analyzing the network usage information, wherein the plurality of subjects comprises topics;

analyze the network usage information of the user to determine a set of relationship scores, wherein each relationship score describes the strength of a relationship between the user and an individual subject in the plurality of subjects;

analyze the set of relationship scores and the relationship scores of other users to detect a plurality of virtual communities, wherein the user may be a member of more than one virtual community, and wherein the analyzing the set of relationship scores further comprises;

generating a user-topic matrix comprising the set of relationship scores for the user and the relationship scores of the other users within the virtual communities, wherein the user-topic matrix identifies one or more topics which are most strongly related with the user and each of the other users, and

applying a topic model to the user-topic matrix in order to generate a topic-community matrix and a user-community matrix, the topic model comprising a Dirichlet allocation model, wherein the topic-community matrix identifies for each topic one or more virtual communities which are most strongly associated with the topic, and the user-community matrix identifies one or more virtual communities which are most strongly associated with the user and in which the user is considered a member;

in response to a determination that the user is a member of at least one of the virtual communities, recommend one or more content items stored in said memory to the user based on the virtual communities of which the user is a member; and

in response to a determination that the user has taken an action related to the network usage information;

determine whether the action taken by the user is a direct action that directly relates to a content item in the storage area;

in response to a determination that the action taken is a direct action, recommend one or more other content items stored in the storage area to the user based at least partially upon at least one topic associated with the content item upon which the direct action is based; and

in response to a determination that the action taken is not a direct action, recommend one or more other content items stored in the storage area to the user based at least partially upon at least one topic associated with the action taken by the user.

11. The one or more computer readable storage devices of claim 10, further comprising instructions that when executed cause the processor to:

update virtual community membership by the computer repeating said obtaining, detecting, analyzing the network usage information and analyzing the set of relationship scores steps, wherein said updating may result in changes in one or more relationship scores for the user, and the user being added or deleted as a member of one or more virtual communities.

12. The one or more computer readable storage devices of claim 10, wherein said analyzing the set of relationship scores comprises further instructions that when executed cause the processor to:

apply a statistical correlation model to the set of relationship scores and the relationship scores of the other users in order to determine which of the other users are statistically correlated with the user, wherein the statistical correlation model is a similarity model or a k-nearest neighbors model; and

assign the user to the same virtual community as the other users who are statistically correlated with the user.

13. The one or more computer readable storage devices of claim 10, wherein said recommendation further comprises, for each virtual community of which a user is a member, instructions that when executed cause the processor to:

determine one or more subjects that have the highest relationship scores to other members of the same virtual community; and

use the determined subjects to identify one or more content items associated with the determined subjects to recommend to the user.