Method and system for adapting search results to personal information needs

• US 7,849,089 B2
• Filed: 11/11/2009
• Issued: 12/07/2010
• Est. Priority Date: 05/10/2005
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Jiang, Xue-Mei, Xue, Gui-Rong, Ma, Wei-Ying, Chen, Zheng, Zhang, Benyu, Zeng, Hua-Jun
• Primary Examiner(s): Jalil; Neveen Abel
• Assistant Examiner(s): Chbouki; Tarek
• Application Number:
  US12/616,739
• Publication Number:
  US 20100057798A1
• Time in Patent Office: 391 Days
• Field of Search: None
• US Class Current: 707/737
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9558 Details of hyperlinks; Management of linked annotations
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

A method and system for adapting search results of a query to the information needs of the user submitting the query is provided. A search system analyzes click-through triplets indicating that a user submitted a query and that the user selected a document from the results of the query. To overcome the large size and sparseness of the click-through data, the search system when presented with an input triplet comprising a user, a query, and a document determines a probability that the user will find the input document important by smoothing the click-through triplets. The search system then orders documents of the result based on the probability of their importance to the input user.

Claims

1. A method in a computer system for calculating importance of a document, the method comprising:

providing user, query, and document triplets indicating that the user submitted the query and that the user selected the document from a result of the query;

receiving an input user, an input query, and an input document as an input triplet, the input query having been submitted by the input user and the input document being a document of a search result for the input query; and

determining a probability that the user will find the input document important by performing a smoothing of the provided triplets to account for triplets not provided and calculating the probability based on the smoothing wherein the determined probability is based at least in part on the provided triplets for users other than the input user and wherein the probability is the probability of the input document given the input user and the input query when the corresponding user, query, and document triplet is provided and is the probability of the input document given the input query otherwise and wherein a back-off factor is applied to the probability.

2. The method of claim 1 wherein the probability is based on the probability that the input document is in a document cluster and the probability of the document cluster given a user cluster and query cluster.

3. The method of claim 1 wherein the probability is based on probabilities of clusters identified based on relationships between users, queries, and documents.

4. The method of claim 1 wherein the probability is based on similarity between an input query and query clusters identified based on relationships between users and documents.

5. The method of claim 1 wherein the probability is based on similarity between an input document and document clusters identified based on relationships between users and queries.

6. The method of claim 1 wherein the smoothing uses a combination of back-off, clustering, and content similarity techniques.

7. A computer-readable storage medium containing instructions for controlling a computer system to calculate relevance of a document to a user, by a method comprising:

providing click-through data generated when users submitted queries to a search engine and selected a document from results provided by the search engine;

identifying user, query, and document triplets from the click-through data, each triplet indicating that the user of the triplet submitted the query of the triplet and the user selected the document of the triplet from results of the query provided by the search engine;

receiving from a user a query;

searching for documents to be provided as results of the received query;

for each document of the results of the received query, determining a probability that the user from whom the query was received will find the document relevant by performing a smoothing of the identified triplets to account for sparseness of the triplets and calculating the probability based on the smoothed triplets, wherein the determined probability is based at least in part on identified triplets identified for users other than the user from whom the query was received and wherein the probability is the probability of the document given the user and the query when the corresponding user, query, and document triplet is identified and is the probability of the document given the query otherwise and wherein a back-off factor is applied to the probability; and

displaying an indication of the documents to the user from whom the query was received in an order based on the determined probabilities of the documents.

8. The computer-readable storage medium of claim 7 including processing the triplets to identify clusters of users, queries, and documents.

9. The computer-readable medium of claim 7 including processing the triplets to calculate various counts.

10. The computer-readable storage medium of claim 7 wherein the probability is based on the probability of the input document given a document cluster and the probability of the document cluster given a user cluster and query cluster.

11. The computer-readable storage medium of claim 7 wherein

the probability is based on similarity between the input

document and document clusters identified based on relationships between users and queries.

12. The computer-readable storage medium of claim 7 wherein the smoothing uses a combination of back-off, clustering, and content similarity techniques.

13. A computer system for calculating relevance of a document, comprising:

a click-through data store providing click-through data generated when users submitted queries to a search engine and selected a document from results provided by the search engine;

a memory containing computer-executable instructions of

a component that identifies user, query, and document triplets from the click-through data, each triplet indicating that the user of the triplet submitted the query of the triplet and the user selected the document of the triplet from results of the query provided by the search engine;

a component that receives an input user, an input query, and input documents as an input triplet; and

a component that determines a probability that the input user will find each input document relevant by performing a smoothing of the identified triplets to account for sparseness of the triplets and calculating the probability based on the smoothed triplets, wherein the determined probability is based at least in part on identified triplets identified for users other than the input user and wherein the probability is the probability of an input document given the input user and the input query when the corresponding user, query, and document triplet is identified and is the probability of the input document given the input query otherwise and wherein a back-off factor is applied to the probability; and

a processor for executing the computer-executable instructions stored in the memory.

14. The computer system of claim 13 including a component to identify clusters of users, queries, and documents based on relationships between users, queries, and documents.

15. The computer system of claim 13 wherein the probability is based on the probability of the input document given a document cluster and the probability of the document cluster given a user cluster and a query cluster.

16. The computer system of claim 13 wherein the probability is based on similarity between an input document and document clusters identified based on relationships between users and queries.