Automatic, personalized online information and product services

• US 7,685,276 B2
• Filed: 01/08/2008
• Issued: 03/23/2010
• Est. Priority Date: 12/28/1999
• Status: Expired due to Term

• Current Assignee: Personalized User Model LLP
• Original Assignee: Personalized User Model LLP
• Inventors: Konig, Yochai, Twersky, Roy, Berthold, Michael R.
• Primary Examiner(s): BAROT, BHARAT
• Application Number:
  US12/008,148
• Publication Number:
  US 20080114756A1
• Time in Patent Office: 805 Days
• Field of Search: 709201-203, 709223-228, 707 1- 3, 707 7- 10, 715736-743
• US Class Current: 709/224
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/53 using third party service providers
• CPC Class Codes: H04L 67/55 Push-based network services
• CPC Class Codes: H04L 67/75 Indicating network or usage conditions on the user display
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing

Abstract

A method for providing automatic, personalized information services to a computer user includes the following steps: transparently monitoring user interactions with data during normal use of the computer; updating user-specific data files including a set of user-related documents; estimating parameters of a learning machine that define a User Model specific to the user, using the user-specific data files; analyzing a document to identify its properties; estimating the probability that the user is interested in the document by applying the document properties to the parameters of the User Model; and providing personalized services based on the estimated probability. Personalized services include personalized searches that return only documents of interest to the user, personalized crawling for maintaining an index of documents of interest to the user; personalized navigation that recommends interesting documents that are hyperlinked to documents currently being viewed; and personalized news, in which a third party server customized its interaction with the user. The User Model includes continually-updated measures of user interest in words or phrases, web sites, topics, products, and product features. The measures are updated based on both positive examples, such as documents the user bookmarks, and negative examples, such as search results that the user does not follow. Users are clustered into groups of similar users by calculating the distance between User Models.

Claims

1. A computer-implemented method for providing personalized information services to a user, the method comprising:

transparently monitoring user interactions with data while the user is engaged in normal use of a browser program running on the computer;

analyzing the monitored data to determine documents of interest to the user;

estimating parameters of a user-specific learning machine based at least in part on the documents of interest to the user;

receiving a search query from the user;

retrieving a plurality of documents based on the search query;

for each retrieved document of said plurality of retrieved documents;

identifying properties of the retrieved document, and applying the identified properties of the retrieved document to the user-specific learning machine to estimate a probability that the retrieved document is of interest to the user; and

using the estimated probabilities for the respective plurality of retrieved documents to present at least a portion of the retrieved documents to the user.

2. The method of claim 1, further comprising presenting to said user a list of said portion of the retrieved documents.

3. The method of claim 1, wherein transparently monitoring user interactions with data comprises monitoring user interactions with data during multiple different modes of user interaction with network data.

4. The method of claim 3, wherein the multiple different modes of user interaction comprise a plurality of modes selected from the group consisting of a network searching mode, a network navigation mode, and a network browsing mode.

5. The method of claim 1, further comprising analyzing the monitored data to determine documents not of interest to the user, and wherein estimating parameters of a user-specific learning machine further comprises estimating parameters of a user-specific learning machine based at least in part on the documents not of interest to the user.

6. The method of claim 1, wherein monitoring user interactions with data for a document comprises monitoring at least one type of data selected from the group consisting of information about the document, whether the user viewed the document, information about the user'"'"'s interaction with the document, context information, the user'"'"'s degree of interest in the document, time spent by the user viewing the document, whether the user followed at least one link contained in the document, and a number of links in the document followed by the user.

7. The method of claim 1, wherein said plurality of retrieved documents correspond to a respective plurality of products.

8. The method of claim 7, wherein using the estimated probabilities to present at least a portion of the retrieved documents to the user comprises presenting at least a portion of said products to the user.

9. The method of claim 1, wherein said search query pertains to a product of interest to the user, and wherein retrieving said plurality of documents based on the search query comprises retrieving a plurality of documents pertaining to a plurality of products related to the product of interest to the user.

10. The method of claim 9, wherein applying the identified properties of the retrieved document comprises applying the identified properties of the retrieved document pertaining to said related product to the user-specific learning machine to estimate a probability that the related product is of interest to the user.

11. The method of claim 10, wherein using the estimated probabilities for the respective plurality of retrieved documents comprises using the estimated probabilities for the respective plurality of retrieved documents pertaining to the related products to present at least a portion of the related products to the user.

12. The method of claim 1, further comprising estimating parameters of said user-specific learning machine based on a set of initial parameters identified at least in part on initial documents associated with said browser program.

13. The method of claim 12, wherein said initial documents are selected from the group of files consisting of favorites, bookmarks, cached files, temporary Internet files, and browsing history.

14. The method of claim 1, wherein identifying properties of the retrieved document comprises determining whether at least one of said documents of interest contains a link to said retrieved document.

15. The method of claim 1, wherein at least one of said properties of the retrieved document is based on intermediate documents linking from at least one of said documents of interest to said user towards said retrieved document.

16. The method of claim 15, wherein identifying properties of the retrieved document further comprises estimating a probability that at least one of said intermediate document linking from at least one of said documents of interest to said user towards said retrieved document are of interest to the user.

17. The method of claim 1, wherein identifying properties of the retrieved document further comprises estimating a probability that at least one intermediate document linking from at least one of said documents of interest to said user towards said retrieved document are of interest to the user.

18. The method of claim 1, wherein analyzing the monitored data to determine documents of interest to the user comprises analyzing said monitored data to obtain data associated with said monitored data selected from the group consisting of text, images, non-text media, and formatting.

19. The method of claim 18, wherein identifying properties of the retrieved document comprises analyzing said retrieved document to obtain data associated with the retrieved document said associated data selected from the group consisting of text, images, non-text media, and formatting.

20. The method of claim 19, wherein applying the identified properties of the retrieved document to the user-specific learning machine comprises comparing said data associated with said retrieved document with data in said user-specific learning machine having a type corresponding thereto.

21. The method of claim 1, wherein using the estimated probabilities for the respective plurality of retrieved documents to present at least a portion of the retrieved documents to the user comprises presenting to the user at least said portion of the retrieved documents based on the estimated probability that the retrieved document is of interest to the user and the relevance of the retrieved document to the search query.

22. The method of claim 1, wherein identifying properties of the retrieved document comprises identifying properties selected from the properties consisting of a topic associated with the retrieved document, at least one product feature extracted from the retrieved document, an author of the retrieved document, an age of the retrieved document, a list of documents linked to the retrieved document, a number of users who have accessed the retrieved document, and a number of users who have saved the retrieved document in a favorite document list.

23. A computer-implemented method for providing personalized information services to a user, the method comprising:

transparently monitoring user interactions with data while the user is engaged in normal use of a browser program running on the computer;

analyzing the monitored data to determine documents of interest to the user;

estimating parameters of a user-specific learning machine based at least in part on the documents of interest to the user;

collecting a plurality of documents of interest to a user;

for each of said plurality of collected documents;

identifying properties of the collected document, and applying the identified properties of the collected document to the user-specific learning machine to estimate a probability that the collected document is of interest to the user;

using the estimated probabilities for the respective plurality of collected documents to select at least a portion of the collected documents;

presenting said selected collected documents to said user.

24. The method of claim 23, wherein presenting said selected collected documents to said user comprises displaying said selected collected documents to said user on a personal web page associated with the user.

25. The method of claim 23, wherein said plurality of collected documents correspond to a respective plurality of products.

26. The method of claim 25, wherein using the estimated probabilities to present at least a portion of the retrieved documents to the user comprises presenting at least a portion of said products to the user.

27. The method of claim 24, wherein analyzing the monitored data to determine documents of interest to the user comprises analyzing said monitored data to obtain data associated with said monitored data selected from the group consisting of text, images, non-text media, and formatting.

28. The method of claim 27, wherein identifying properties of the collected document comprises analyzing said collected document to obtain data associated with the collected document said associated data selected from the group consisting of:

text, images, non-text media, and formatting.

29. The method of claim 28, wherein applying the identified properties of the collected document to the user-specific learning machine comprises comparing said data associated with said collected document with data in said user-specific learning machine having a type corresponding thereto.