GENERATING RANKED SEARCH RESULTS USING LINEAR AND NONLINEAR RANKING MODELS

• US 20140351246A1
• Filed: 06/04/2014
• Published: 11/27/2014
• Est. Priority Date: 06/19/2009
• Status: Active Grant

• Current Assignee: Alibaba Group Holding Ltd.
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: Guo, Ning, Xing, Fei, Xie, Yuheng, Hou, Lei, Zhang, Qin
• Granted Patent: US 9,471,643 B2
• Time in Patent Office: Days
• US Class Current: 707/723
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/30 of unstructured textual data document management systems G06F16/93
• CPC Class Codes: G06F 16/438 Presentation of query results
• CPC Class Codes: G06F 16/44 Browsing; Visualisation therefor
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/957 Browsing optimisation, e.g. caching or content distillation

Abstract

Generating ranked search results includes receiving a plurality of matching information items that match a search request, ranking at least some of the plurality of matching information items using a linear ranking model that linearly combines a first plurality of feature values to obtain a first set of ranked results, ranking at least some of the first set of ranked results using a nonlinear ranking model that nonlinearly combines a second plurality of feature values to obtain a second set of ranked results, and provide a search response based on the second set of ranked results.

Claims

1. (canceled)

2. A system, comprising:

a first unit configured to obtain a first set of ranked results by using a first ranking model to determine first rank scores corresponding to respective ones of at least some of a plurality of matching information items;

a second ranking unit configured to;

receive the first set of ranked results from the first ranking unit; and

obtain a second set of ranked results by using a second ranking model to determine second rank scores corresponding to respective ones of at least some of the first set of ranked results; and

a result generating unit configured to generate a search response based at least in part on the second set of ranked results.

3. The system of claim 2, wherein the plurality of matching information items comprises a second plurality of matching information items and wherein the first ranking unit is further configured to:

receive a search request;

generate a first plurality of matching information items based at least in part on the search request; and

select from the first plurality of matching information items the second plurality of matching information items.

4. The system of claim 3, wherein the first plurality of matching information items includes a greater number of matching information items than the second plurality of matching information items.

5. The system of claim 2, wherein determining the first rank scores corresponding to the respective ones of the at least some of the plurality of matching information items includes linearly combining a first plurality of feature values associated with the at least some of the plurality of matching information items.

6. The system of claim 5, wherein the first plurality of feature values are determined by one or both of the plurality of matching information and a search request.

7. The system of claim 2, wherein determining the second rank scores corresponding to the respective ones of the at least some of the first set of ranked results includes nonlinearly combining a second plurality of feature values associated with the at least some of the first set of ranked results.

8. The system of claim 2, wherein the second ranking model is determined by training

9. The system of claim 2, wherein the first ranking model includes a constant or a first degree term.

10. The system of claim 2, wherein the second ranking model includes a term that is not associated with a constant or a first order term.

11. The system of claim 2, wherein the first set of ranked results includes a greater number of results than the second set of ranked results.

12. The system of claim 2, wherein the first ranking model comprises a linear ranking model and the second ranking model comprises a nonlinear ranking model.

13. A method, comprising:

obtaining a first set of ranked results by using a first ranking model to determine first rank scores corresponding to respective ones of at least some of a plurality of matching information items;

obtaining a second set of ranked results by using a second ranking model to determine second rank scores corresponding to respective ones of at least some of the first set of ranked results; and

generating a search response based at least in part on the second set of ranked results.

14. The method of claim 13, wherein the plurality of matching information items comprises a second plurality of matching information items and the method further comprising:

receiving a search request;

generating a first plurality of matching information items based at least in part on the search request; and

selecting from the first plurality of matching information items the second plurality of matching information items.

15. The method of claim 14, wherein the first plurality of matching information items includes a greater number of matching information items than the second plurality of matching information items.

16. The method of claim 13, wherein determining the first rank scores corresponding to the respective ones of the at least some of the plurality of matching information items includes linearly combining a first plurality of feature values associated with the at least some of the plurality of matching information items.

17. The method of claim 16, wherein the first plurality of feature values are determined by one or both of the plurality of matching information and a search request.

18. The method of claim 13, wherein determining the second rank scores corresponding to the respective ones of the at least some of the first set of ranked results includes nonlinearly combining a second plurality of feature values associated with the at least some of the first set of ranked results.

19. The method of claim 13, wherein the second ranking model is determined by training.

20. The method of claim 13, wherein the first ranking model includes a constant or a first degree term.

21. The method of claim 13, wherein the second ranking model includes a term that is not associated with a constant or a first order term.

22. The method of claim 13, wherein the first set of ranked results includes a greater number of results than the second set of ranked results.