RANKING MODEL ADAPTATION FOR SEARCHING

• US 20090276414A1
• Filed: 04/30/2008
• Published: 11/05/2009
• Est. Priority Date: 04/30/2008
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Wu, Qiang, Chen, Junyan, Song, Jiangyun, Yao, Steven, Gao, Jianfeng
• Application Number:
  US12/112,826
• Publication Number:
  US 20090276414A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

Search results provided by a search engine (e.g., for the Internet) are improved and/or made more accurate by addressing the limited availability of human labeled training data for certain domains (e.g., languages other than English, within certain date ranges, corresponding to queries over a certain length, etc.). More particularly, a ranking model trained on in-domain data, for which a small amount of human labeled training data (e.g., query/URL pairs) is available (e.g., languages other than English) is adjusted based upon out-domain data, for which a large amount of human labeled training data (e.g., query/URL pairs) is available (e.g., English). Thus, even though the resulting adapted in-domain ranking model is used in the context of in-domain data (e.g., non-English) to provide search results, the search results are improved because they are influenced by an abundance of, albeit out-domain, human labeled training data.

Claims

1. A method for adapting a ranking model, comprising:

obtaining one or more in-domain ranking models comprising a plurality of feature functions which map a query/URL pair to a first real number relevance score;

obtaining one or more out-domain ranking models comprising a plurality of feature functions which map the query/URL pair to a second real number relevance score;

training the in-domain ranking models and the out-domain ranking models;

assigning respective weighting factors to trained in-domain ranking models and trained out-domain ranking models;

enhancing the weighting factors using in-domain data according to an adaptation method; and

combining the enhanced weighted trained in-domain ranking models and the enhanced weighted trained out-domain ranking models to form an adapted in-domain ranking model which maps the query/URL pair to a third real number relevance score.

2. The method of claim 1, training the in-domain ranking models comprising using in-domain training data and training the out-domain ranking models comprising using out-domain training data.

3. The method of claim 2, the adaptation method comprising model interpolation.

4. The method of claim 3, the adapted in-domain ranking model comprising a linear combination of the enhanced weighted trained in-domain ranking models and the enhanced weighted trained out-domain ranking models.

5. The method of claim 4, the in-domain training data used to train the in-domain ranking model not overlapping the in-domain data used for enhancing the weighting factors using in-domain data according to an adaptation method.

6. The method of claim 5, the model interpolation comprising a neural network ranker using an implicit cost function whose gradients are specified by rules.

7. The method of claim 5, the model interpolation comprising a coordinate enhancement method.

8. The method of claim 5, the model interpolation utilizing the Powell algorithm.

9. The method of claim 5, the in-domain ranking models comprising a first language and the out-domain ranking models comprising one or more languages different than the first language.

10. A system configured to improve a relevance of Web searches for a query comprising:

a data structure configured to store a plurality of URLs;

an adapted in-domain ranking component configured to rank a plurality of query/URL pairs returned in response to the query, the adapted in-domain ranking component comprising a combination of one or more enhanced weighted trained in-domain ranking models and one or more enhanced weighted trained out-domain ranking models; and

a processing component configured to operate the adapted in-domain ranking model on candidate URLs from the data structure.

11. The system of claim 10, the adapted in-domain ranking model comprising respective weighting factors assigned to the enhanced weighted trained in-domain and enhanced weighted trained out-domain ranking models.

12. The system of claim 11, the enhanced weighted trained in-domain ranking models trained using in-domain training data and the enhanced weighted trained out-domain ranking models trained using out-domain training data.

13. The system of claim 12, the respective weighting factors enhanced using model interpolation using in-domain data.

14. The system of claim 13, the in-domain training data used to train the in-domain ranking model not overlapping the in-domain data used for enhancing the weighting factors.

15. The system of claim 14, the model interpolation comprising a neural network ranker using an implicit cost function whose gradients are specified by rules.

16. The system of claim 14, the model interpolation comprising a coordinate enhancement method.

17. The system of claim 14, the model interpolation utilizing the Powell algorithm.

18. The system of claim 14, the adapted in-domain ranking model comprising a linear combination of the enhanced weighted trained in-domain ranking models and the enhanced weighted trained out-domain ranking models.

19. The system of claim 14, the data structure comprising an index.

20. A method for adapting a ranking model, comprising:

obtaining one or more in-domain ranking models comprising a plurality of feature functions which map a query/URL pair to a first real number relevance score;

forming one or more out-domain ranking models comprising a plurality of feature functions which map the query/URL pair to a second real number relevance score;

training the in-domain ranking models using in-domain training data and training the out-domain ranking models using out-domain training data;

assigning respective weighting factors to trained in-domain ranking models and trained out-domain ranking models;

enhancing the weighting factors using in-domain data according to an interpolation method comprising at least one of a neural network ranker, a coordinate enhancement method, and the Powell algorithm; and

combining the enhanced weighted trained in-domain ranking models and the enhanced weighted trained out-domain ranking models to form an adapted in-domain ranking model which maps the query/URL pair to a third real number relevance score.