Proactive Content Dissemination to Users

• US 20090112837A1
• Filed: 10/24/2007
• Published: 04/30/2009
• Est. Priority Date: 10/24/2007
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Parija, Gyana Ranjan, Modani, Natwar, Mukerjea, Sougata, Saronwala, Anupam
• Application Number:
  US11/923,100
• Publication Number:
  US 20090112837A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

A content repository of a system stores items of content to be disseminated to users. The content repository generates a content profile for each item of content as the item of content is received. The content profile for each item of content includes information regarding the item of content. A user repository of the system generates and stores a user profile for each user. The user profiles are generated from one or more information sources. The user profile for each user includes information regarding the user. A recommendation engine of the system determines which items of content should be delivered to each user based on the content profiles of the items of content and on the user profile of each user, to yield relevant items of content for each user. The recommendation engine then delivers the relevant items of content to each user.

Claims

1. A system comprising:

a content repository configured to store a plurality of items of content to be disseminated to users, and store a content profile for each item of content as the item of content is received by the content repository, the content profile for each item of content including information regarding the item of content;

a user repository configured to generate and store a user profile for each user, the user profile for each user generated from one or more information sources, the user profile for each user including information regarding the user; and

,

a recommendation engine configured to determine which of the plurality of items of content should be delivered to each user based on the content profiles of the plurality of items of content and on the user profile of each user, to yield relevant items of content for each user, the recommendation engine to deliver the relevant items of content to each user,

wherein delivery of the relevant items of content to each user is triggered by changes in the user profile for the user.

2. The system of claim 1, all the limitations of which are incorporated herein by reference, wherein delivery of the relevant items of content to each user is further triggered by additions or changes of the plurality of items of content stored within the content repository.

3. The system of claim 1, all the limitations of which are incorporated herein by reference, wherein the user profile for each user is dynamically generated, such that the user profile for the user automatically changes as new information regarding the user is obtained, without direct interaction with the user.

4. The system of claim 3, all the limitations of which are incorporated herein by reference, wherein the user profile for the user automatically changes as new information regarding the user is obtained, without direct interaction with the user, and such that a history of the changes made to the user profile are maintained and become part of the user profile.

5. The system of claim 1, all the limitations of which are incorporated herein by reference, wherein the user repository is to generate the user profile for each user based at least on behavior of the user.

6. The system of claim 1, all the limitations of which are incorporated herein by reference, wherein the user repository is to generate the user profile for each user based at least on one or more of structured information sources and semi-structured information sources within which information regarding the user is located, the information comprising one or more of roles and responsibilities of the users, identified KPI'"'"'s for the user, work and/or other documents of the user, and a defect queue of the user for support personnel.

7. The system of claim 1, all the limitations of which are incorporated herein by reference, wherein the user repository is to generate the user profile for each user based at least on explicit inputs provided by the user.

8. The system of claim 1, all the limitations of which are incorporated herein by reference, wherein the user repository is to generate the user profile for each user based at least on a history of the user.

9. The system of claim 1, all the limitations of which are incorporated herein by reference, wherein the recommendation engine is to determine which of the plurality of items of content should be delivered to each user based at least on matching the content profiles of the plurality of items of content with information needs encompassed by the user profiles.

10. The system of claim 1, all the limitations of which are incorporated herein by reference, wherein the recommendation engine is to determine which of the plurality of items of content should be delivered to each user by each of a plurality of recommendation subsystems of the recommendation engine at least assigning each item of recommended content for each user a confidence score and an accuracy score, the confidence score denoting a confidence of relevance of the item of content for the user, the accuracy score denoting an accuracy of how relevant the item of content is for the user, the recommendation engine determining the relevant items of content for the user based on a balance of the confidence score and the accuracy score.

11. The system of claim 10, all the limitations of which are incorporated herein by reference, wherein the recommendation engine is to determine which of the plurality of items of content should be delivered to each user by at least employing a regression model combining the recommended content from the recommendation subsystems.

12. The system of claim 10, all the limitations of which are incorporated herein by reference, wherein the recommendation engine is to adjust weights assigned to the recommendation subsystems by at least incorporating user feedback as to how relevant previously delivered items of content from the recommendation subsystems are.

13. The system of claim 1, all the limitations of which are incorporated herein by reference, wherein the recommendation engine is to determine which of the plurality of items of content should be delivered to each user by each of a plurality of recommendation subsystems of the recommendation engine by employing a different one of:

a rule-based engine, a collaborative-filtering model, and a peer group model.

14. A method comprising:

generating a content profile for each item of content from a plurality of items of content to be disseminated to users, as the item of content is received;

dynamically generating a user profile for each user, such that the user profile for the user automatically changes as new information regarding the user is obtained, without direct interaction with the user, such that a history of the changes made to the user profile are maintained and become part of the user profile;

in response to one or more of an addition to the plurality of items of content, a change to the plurality of items of content, and a change in the user profiles of the users;

determining which of the plurality of items of content should be delivered to each user based on the content profiles of the plurality of items of content and on the user profiles of the users, to yield relevant items of content for each user; and

,

delivering the relevant items of content determined for each user to the respective user.

15. The method of claim 14, all the limitations of which are incorporated herein by reference, wherein dynamically generating the user profile for each user is based on a set of predefined parameters comprises at least one of:

structured information sources within which information regarding the user is located;

semi-structured information sources within which information regarding the user is located;

a history of the user;

or,

explicit inputs provided by the user.

16. The method of claim 14, all the limitations of which are incorporated herein by reference, wherein determining which of the plurality of items of content should be delivered to each user further comprises matching the content profiles of the plurality of items of content with information needs encompassed by the user profiles.

17. The method of claim 14, all the limitations of which are incorporated herein by reference, wherein determining which of the plurality of items of content should be delivered to each user is based on one or more of:

assigning each item of content for each user a confidence score and an accuracy score, and determining the relevant items of content for the user based on a balance of the confidence score and the accuracy score, the confidence score denoting a confidence of relevance of the item of content for the user, the accuracy score denoting an accuracy of how relevant the item of content is for the user;

employing a regression model combining recommended content from a number of content sources;

incorporating user feedback on relevancy of previously delivered items of content are;

employing a rule-based engine;

employing a collaborative-filtering model; and

,

employing a peer group model.

18. A computer-readable medium having one or more computer programs stored thereon to perform a method comprising:

generating a content profile for each item of content of a plurality of items of content to be disseminated to users, as the item of content is received;

dynamically generating a user profile for each user, such that the user profile for the user automatically changes as new information regarding the user is obtained, without direct interaction with the user, such that a history of the changes made to the user profile are maintained and become part of the user profile;

in response to one or more of an addition to the plurality of items of content, a change to the plurality of items of content, and a change in the user profiles of the users;

determining which of the plurality of items of content should be delivered to each user based on the content profiles of the plurality of items of content and on the user profiles of the users to yield relevant items of content for each user, by at least matching the content profiles of the plurality of items of content with information needs encompassed by the user profiles; and

,

delivering the relevant items of content for each user to the user.

19. The computer-readable medium of claim 18, all the limitations of which are incorporated herein by reference, wherein dynamically generating the user profile for each user is based on one or more of:

structured information sources within which information regarding the user is located;

semi-structured information sources within which information regarding the user is located;

a history of the user; and

,

explicit inputs provided by the user.

20. The computer-readable medium of claim 18, all the limitations of which are incorporated herein by reference, wherein determining which of the plurality of items of content should be delivered to each user is based on one or more of:

assigning each item of content for each user a confidence score and an accuracy score, and determining the relevant items of content for the user based on a balance of the confidence score and the accuracy score, the confidence score denoting a confidence of relevance of the item of content for the user, the accuracy score denoting an accuracy of how relevant the item of content is for the user;

employing a regression model combining recommended content from a number of content sources;

incorporating user feedback as to how relevant previously delivered items of content are;

employing a rule-based engine;

employing a collaborative-filtering model; and

,

employing a peer group model.