Multi-directional and auto-adaptive relevance and search system and methods thereof

• US 20070250500A1
• Filed: 12/05/2006
• Published: 10/25/2007
• Est. Priority Date: 12/05/2005
• Status: Abandoned Application

• Current Assignee: Collarity Incorporated
• Original Assignee: Collarity Incorporated
• Inventors: Ismalon, Emil
• Application Number:
  US11/633,461
• Publication Number:
  US 20070250500A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

The multi-directional and auto-adaptive relevance and search methods hereof are capable of clustering information and users in ways that allow for higher quality search results to be provided to all the users of the system. As part of the operation of the search engine, both information pages and users are clustered in meaningful ways using multi-layer association graphs. Specifically, a multi-directional approach is used to allow the transfer of information from the users to the information pages in addition to the traditional transfer of data from the information pages to the user. The clustering is performed with respect to the identification of clusters of plurality of users that enables the information pages clustering in a dynamic way providing additional refinements beyond user profiles. Furthermore, the system is configured to provide personalized advisory by presenting additional search phrases tailored to the searching user.

Claims

1-10. -10. (canceled)

11. A computer-implemented method comprising:

generating at least one association graph;

receiving a search phrase from a user;

using the at least one association graph, generating a set of advisory keywords associated with the search phrase;

presenting the set of advisory keywords to the user;

responsively to a selection of at least one of the advisory keywords by the user, adding the selected at least one advisory keywords to the search phrase to generate a revised search phrase;

generating search results responsively to the revised search phrase; and

presenting the search results to the user.

12. The method according to claim 11, wherein generating the association graph comprises generating a personal association graph (PAG) that reflects associations of search keywords based on interactions of the user with information pages during previous searches performed by the user, and wherein generating the set of advisory keywords comprises generating the set of advisory keywords using the PAG.

13. The method according to claim 11, wherein the user is one of a plurality of users, wherein generating the association graph comprises generating a topic association graph (TAG) that reflects associations of search keywords relating to a single topic based on interactions of the plurality of users with information pages during previous searches performed by the users, and wherein generating the set of advisory keywords comprises generating the set of advisory keywords using the TAG.

14. The method according to claim 11, wherein the user is one of a plurality of users, wherein generating the association graph comprises generating a global association graph (GAG) that reflects associations of search keywords based on interactions of the plurality of users with information pages during previous searches performed by the users, and wherein generating the set of advisory keywords comprises generating the set of advisory keywords using the GAG.

15. The method according to claim 11, wherein generating the set of advisory keywords comprises generating the set of advisory keywords responsively to a level of association of the search phrase with the search keywords in the at least one association graph.

16. The method according to claim 11, wherein generating the set of advisory keywords comprises:

identifying a context of the search phrase;

constructing an association tree by analyzing clusters of documents having the same context as the search phrase; and

generating the set of advisory keywords using the at least one association graph and the association tree.

17. The method according to claim 11, wherein generating the set of advisory keywords comprises generating the set of advisory keywords using a plurality of association graphs, and wherein presenting the set of advisory keywords comprises presenting highest ranking advisory keywords from each of the association graphs.

18. The method according to claim 11,

wherein generating the search results comprises generating a list of relevant URLs of information pages, and

wherein presenting the search results to the user comprises:

creating a user query matrix based on the revised search phrase and a personal association graph (PAG) of the user that reflects associations of search keywords based on interactions of the user with information pages during previous searches performed by the user;

creating respective URL query matrices for the relevant URLs;

computing respective relevancy scores of each of the URL query matrices to the user query matrix;

sorting the list of relevant URLs in descending order according to the respective relevancy scores; and

presenting at least a top-ranked portion of the ordered URL list to the user.

19. Apparatus comprising:

an interface for communicating with a user; and

a processor, which is configured to generate at least one association graph;

receive a search phrase from a user, via the interface;

using the at least one association graph, generate a set of advisory keywords associated with the search phrase;

present the set of advisory keywords to the user, via the interface;

responsively to a selection of at least one of the advisory keywords by the user, add the selected at least one advisory keywords to the search phrase to generate a revised search phrase;

generate search results responsively to the revised search phrase; and

present the search results to the user, via the interface.

20. The apparatus according to claim 19, wherein the processor is configured to generate a personal association graph (PAG) that reflects associations of search keywords based on interactions of the user with information pages during previous searches performed by the user, and to generate the set of advisory keywords using the PAG.

21. The apparatus according to claim 19, wherein the user is one of a plurality of users, and wherein the processor is configured to generate a topic association graph (TAG) that reflects associations of search keywords relating to a single topic based on interactions of the plurality of users with information pages during previous searches performed by the users, and to generate the set of advisory keywords using the TAG.

22. The apparatus according to claim 19, wherein the user is one of a plurality of users, and wherein the processor is configured to generate a global association graph (GAG) that reflects associations of search keywords based on interactions of the plurality of users with information pages during previous searches performed by the users, and to generate the set of advisory keywords using the GAG.

23. The apparatus according to claim 19, wherein the processor is configured to generate the set of advisory keywords responsively to a level of association of the search phrase with the search keywords in the at least one association graph.

24. The apparatus according to claim 19, wherein the processor is configured to generate the set of advisory keywords by:

identifying a context of the search phrase, constructing an association tree by analyzing clusters of documents having the same context as the search phrase, and generating the set of advisory keywords using the at least one association graph and the association tree.

25. The apparatus according to claim 19, wherein the processor is configured to generate the set of advisory keywords using a plurality of association graphs, and to present highest ranking advisory keywords from each of the association graphs.

26. The apparatus according to claim 19, wherein the processor is configured to generate a list of relevant URLs of information pages, and to present the search results to the user by:

creating a user query matrix based on the revised search phrase and a personal association graph (PAG) of the user that reflects associations of search keywords based on interactions of the user with information pages during previous searches performed by the user, creating respective URL query matrices for the relevant URLs, computing respective relevancy scores of each of the URL query matrices to the user query matrix, sorting the list of relevant URLs in descending order according to the respective relevancy scores, and presenting at least a top-ranked portion of the ordered URL list to the user.

27. A computer software product, comprising a tangible computer-readable medium in which program instructions are stored, which instructions, when read by a computer, cause the computer to generate at least one association graph;

receive a search phrase from a user;

using the at least one association graph, generate a set of advisory keywords associated with the search phrase;

present the set of advisory keywords to the user;

responsively to a selection of at least one of the advisory keywords by the user, add the selected at least one advisory keywords to the search phrase to generate a revised search phrase;

generate search results responsively to the revised search phrase; and

present the search results to the user.

28. The computer software product according to claim 27, wherein the instructions, when read by the computer, cause the computer to generate a personal association graph (PAG) that reflects associations of search keywords based on interactions of the user with information pages during previous searches performed by the user, and to generate the set of advisory keywords using the PAG.

29. The computer software product according to claim 27, wherein the user is one of a plurality of users, and wherein the instructions, when read by the computer, cause the computer to generate a topic association graph (TAG) that reflects associations of search keywords relating to a single topic based on interactions of the plurality of users with information pages during previous searches performed by the users, and to generate the set of advisory keywords using the TAG.

30. The computer software product according to claim 27, wherein the user is one of a plurality of users, and wherein the instructions, when read by the computer, cause the computer to generate a global association graph (GAG) that reflects associations of search keywords based on interactions of the plurality of users with information pages during previous searches performed by the users, and to generate the set of advisory keywords using the GAG.

31. The computer software product according to claim 27, wherein the instructions, when read by the computer, cause the computer to generate the set of advisory keywords responsively to a level of association of the search phrase with the search keywords in the at least one association graph.

32. The computer software product according to claim 27, wherein the instructions, when read by the computer, cause the computer to generate the set of advisory keywords by:

identifying a context of the search phrase, constructing an association tree by analyzing clusters of documents having the same context as the search phrase, and generating the set of advisory keywords using the at least one association graph and the association tree.

33. The computer software product according to claim 27, wherein the instructions, when read by the computer, cause the computer to generate the set of advisory keywords using a plurality of association graphs, and to present highest ranking advisory keywords from each of the association graphs.

34. The computer software product according to claim 27, wherein the instructions, when read by the computer, cause the computer to generate a list of relevant URLs of information pages, and to present the search results to the user by:

creating a user query matrix based on the revised search phrase and a personal association graph (PAG) of the user that reflects associations of search keywords based on interactions of the user with information pages during previous searches performed by the user, creating respective URL query matrices for the relevant URLs, computing respective relevancy scores of each of the URL query matrices to the user query matrix, sorting the list of relevant URLs in descending order according to the respective relevancy scores, and presenting at least a top-ranked portion of the ordered URL list to the user.