Method and system for a personalized search engine

• US 20060047643A1
• Filed: 08/29/2005
• Published: 03/02/2006
• Est. Priority Date: 08/31/2004
• Status: Abandoned Application

• Current Assignee: Chirag Chaman
• Original Assignee: Chirag Chaman
• Inventors: Chaman, Chirag
• Application Number:
  US11/214,006
• Publication Number:
  US 20060047643A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/832 Query formulation
• CPC Class Codes: G06F 16/835 Query processing
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

A system for personalization of searches comprising a network which is accessible by one or more users; a search engine which locates a result set of documents in response to a search query by a user; a personalization engine which pre-processes said search query to return a personalized result set. A network-based search engine database configured to store data which is ranked according to usage, the data being searchable by a search engine; and a method for personalization of searches using the database to return a personalized result set of documents to a user.

Claims

1. A system for personalization of searches comprising:

a network which is accessible by one or more users;

a search engine which locates a result set of documents in response to a search query by a user;

a personalization engine which pre-processes said search query to return a personalized result set.

2. The system according to claim 1 wherein said personalization engine adds information to said query to personalize it by said user.

3. The system according to claim 1, wherein said personalization engine filters or re-ranks said result set of documents located by said search engine.

4. The system according to claim 1, further comprising an entity index which keeps track of all documents relevant to said user and a document tracker which collects information via access logs and data feeds including documents visited, client/user identifiers, date, time and links within the documents visited, to compute a score for relevance.

5. The system according to claim 4, further comprising an expert document index which stores scores from said document tracker based on access and usage information of all users collectively.

6. The system according to claim 1, further comprising a document relationship index which stores information on the relationship between documents located in response to said search query.

7. The system according to claim 1, further comprising a personalization object index which creates a personalization object for each user.

8. The system according to claim 7, wherein said personalization object index comprises a root set which is the set of all documents relevant to said user and an extended set which is computed for said user by obtaining all documents related or linked to said root set from said document relationship index.

9. The system according to claim 8, further comprising a document classification index which contains information on which class or category a document resides in.

10. A method for personalization of searches comprising:

maintaining a network-based search engine database configured to store data which is relevant to a search query by a user;

sending a search query by said user to said search engine using a computer network;

returning a result set of documents relevant to said search query;

forwarding said result set of documents to a personalization engine for personalization processing of said documents; and

returning a personalized result set of documents to said user.

11. The method according to claim 10, wherein said personalization processing further comprises adding information to said search query and sending the modified query to said search engine.

12. The method according to claim 10, wherein said user is assigned client/user identifiers which are stored in an entity index;

wherein said entity index further tracks all documents relevant to said user and/or client.

13. The method according to claim 10, wherein said documents are either a set of documents which have been seen by said user or a set of documents which have not been seen by said user.

14. The method according to claim 10, wherein each document is given a score computed on the access and usage information of the document by said user.

15. The method according to claim 14, wherein a document tracker collects information via access logs and data feeds including documents visited, client/user identifiers, date, time and links within the documents visited, to compute said score.

16. The method according to claim 12, wherein said entity index further comprises bookmarks, web histories and manual entries of hyper-links relevant to an entity, wherein said entity is a user, a group, a category or a geographic location.

17. The method according to claim 15, wherein an expert document index stores scores from said document tracker based on access and usage information of all users collectively.

18. A network-based search engine database configured to store data which is ranked according to usage, the data being searchable by a search engine.

19. The database according to claim 18, wherein said data is ranked according to link analysis, importance, time-based usage and relevance of the page.

20. The database according to claim 18, wherein said ranking is computed multiple times per hour.