MESSAGE MINING TO ENHANCE RANKING OF DOCUMENTS FOR RETRIEVAL

• US 20080005108A1
• Filed: 06/28/2006
• Published: 01/03/2008
• Est. Priority Date: 06/28/2006
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Ozzie, Raymond E., Platt, John C., Goodman, Joshua T., Brill, Eric D., Hurst-Hiller, Oliver, Horvitz, Eric J.
• Application Number:
  US11/427,314
• Publication Number:
  US 20080005108A1
• Time in Patent Office: Days
• US Class Current: 707/7
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/107 Computer-aided management of electronic mailing [e-mailing]
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

An architecture is provided for data mining of electronic messages to extract information relating to relevancy and popularity of websites and/or web pages for ranking of web pages or other documents. A monitor component monitors information of a message for a reference to a web page or other document, and a ranking component computes rank of the web page based in part on the reference.

Claims

1. A computer-implemented system that facilitates ranking of web pages, comprising:

a monitor component that monitors information of a message for reference to a web page; and

a ranking component that computes rank of the web page based in part on the message reference.

2. The system of claim 1, wherein the message is one of an e-mail message and an instant message, and the reference the monitoring component identifies is an address to a web page.

3. The system of claim 1, wherein the monitor component analyzes message header information and message content.

4. The system of claim 1, wherein the monitor component extracts information related to relevancy and popularity of the web page.

5. The system of claim 4, wherein the popularity of the web page is utilized by a machine learning system to affect the rank of the page.

6. The system of claim 1, further comprising a selection component that selects messages based on at least one of message source information, message destination information, output of a spam filter, and sender presence on a safe sender list.

7. The system of claim 1, further comprising a tracking component that tracks frequency information related to how often the web page reference is forwarded.

8. The system of claim 1, further comprising a tracking component that tracks click-through information related to how often the web page reference is selected.

9. The system of claim 1, further comprising an advertising component that modifies value set for an advertisement based in part on user interaction with the message having the reference.

10. The system of claim 9, wherein the advertisement is displayed as part of the web page associated with the reference.

11. The system of claim 1, further comprising a profile component that generates a profile of a network based on user interaction with the message.

12. A computer-implemented method of ranking web pages, comprising:

monitoring at least one of e-mail and IM messages having web page reference information contained therein;

tracking frequency at which the messages are forwarded based on the web page reference information; and

ranking of the web page reference information as a function of the frequency of at which the e-mail messages are forwarded.

13. The method of claim 12, further comprising tracking click-through rate of the web page reference information within the respective messages.

14. The method of claim 12, further comprising re-ranking the web page reference information based on change in frequency at which the e-mail messages are forwarded.

15. The method of claim 12, further comprising analyzing the reference information for key information, and performing ranking based additionally thereon.

16. The method of claim 12, further comprising;

determining user information associated with the messages; and

identifying a user related to the user information as a priority source of the e-mail messages.

17. The method of claim 12, further comprising analyzing an attachment of the e-mail message for web page reference information.

18. The method of claim 12, further comprising processing the web page reference information of one or more of the e-mail messages to infer intent of a user associated with the one or more of the e-mail messages.

19. A computer-executable system of ranking web information, comprising:

computer-implemented means for selecting e-mail messages having embedded network document linking information;

computer-implemented means for tracking user interaction with the network document linking information; and

computer-implemented means for ranking a web page based on user interaction data related to the user interaction with the network document linking information.

20. The system of claim 19, wherein the network document linking information automatically routes the user to a corresponding network document when the user interaction is a selection action.