ADAPTIVE DISSEMINATION OF PERSONALIZED AND CONTEXTUALLY RELEVANT INFORMATION

• US 20080028036A1
• Filed: 07/31/2006
• Published: 01/31/2008
• Est. Priority Date: 07/31/2006
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Chandrasekar, Raman, Forney, Michael K., Slawson, Dean A.
• Granted Patent: US 7,577,718 B2
• Time in Patent Office: Days
• US Class Current: 709/217
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99937 Sorting

Abstract

An information dissemination system identifies the interests of a user from information derived from various sources, such as, by way of example, the user'"'"'s calendar entries, emails, World Wide Web (web) usage information, documents, enterprise resource planning (ERP) data, and the like. The information dissemination system then creates a profile for the user, and includes in the profile the details about the user'"'"'s interests including the information sources to query, the user events which were analyzed to determine the user'"'"'s interest, the topics derived from the analyzed events, the queries generated for the analyzed events, a language model of terms that are relevant to the user, and other information regarding the user. The information dissemination system then periodically executes the queries against the appropriate information sources, ranks the search results obtained from executing the queries, and renders the ranked search results to the user.

Claims

1. A computer-implemented method for disseminating contextually relevant information based on user interests, the method comprising:

identifying user interests of a user;

automatically generating a user profile based on the user interests, the user profile comprising key terms derived from terms extracted from the user interests and at least one query generated from one or more key terms;

obtaining search results by executing the query against information sources;

ranking the search results; and

rendering the ranked search results to the user.

2. The method of claim 1, wherein at least one user interest is identified from an appointment of the user.

3. The method of claim 1, wherein at least one user interest is identified from an email of the user.

4. The method of claim 1, wherein the key terms are derived based on overall weights corresponding to the terms.

5. The method of claim 4, wherein the overall weight is based in part on a frequency of a term.

6. The method of claim 4, wherein the overall weight is based in part on a weight of a term.

7. The method of claim 4, wherein the overall weight is based in part on relevance of a term to the user interests.

8. The method of claim 1, wherein the search results are ranked based in part on relevance to the user interests.

9. The method of claim 1 further comprising:

receiving feedback from the user regarding the rendered search results; and

incorporating the feedback into the user profile as user interests.

10. A computer-readable medium containing instructions for controlling a computer system to adaptively disseminate contextually relevant information based on user interests of a user, by a method comprising:

identifying user interests from clusters of terms extracted from one or more user emails;

identifying a user event;

identifying topics based on the user event;

for each topic,

identifying clusters of terms for the topic;

identifying key terms in the clusters of terms; and

generating a query based on the key terms;

obtaining search results by executing the queries against information sources;

ranking the search results; and

rendering the ranked search results to the user.

11. The computer-readable medium of claim 10, wherein the key terms are identified based on overall weight of the terms in the cluster of terms.

12. The computer-readable medium of claim 11, wherein the overall weight of a term is based in part on a frequency of the term in the clusters of terms.

13. The computer-readable medium of claim 11, wherein the overall weight of a term is based in part on a weight of the term in the clusters of terms.

14. The computer-readable medium of claim 11, wherein the overall weight of a term is based in part on a relevance of the term in the clusters of terms to the user interests.

15. The computer-readable medium of claim 10 further comprising:

receiving feedback from the user regarding the rendered search results; and

incorporating the feedback into the user interests.

16. The computer-readable medium of claim 10, wherein the query is based at most on the six highest ranked key terms according to overall weight.

17. The computer-readable medium of claim 10, wherein the search results are ranked based in part on static weights corresponding to the search results.

18. The computer-readable medium of claim 10, wherein the search results are ranked based in part on adaptive weights corresponding to the search results.

19. An information dissemination system comprising:

a profiler component that executes on a client and that identifies user interests, that derives key terms from terms extracted from the user interests, and that generates queries from one or more of the key terms, wherein the key terms are derived based on the relevance of the terms to the user interests;

an information garnerer component that executes on a server and that obtains search results from executing the queries against information sources;

a ranker component that executes on the server and that ranks the search results; and

a renderer component that executes on the server and that renders the ranked search results for viewing.

20. The system of claim 19 further comprising a feedback component that executes on the server and that incorporates feedback regarding the search results into the user interests.