RETRIEVAL AND RANKING OF ITEMS UTILIZING SIMILARITY

• US 20080114750A1
• Filed: 11/14/2006
• Published: 05/15/2008
• Est. Priority Date: 11/14/2006
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Saxena, Ashutosh, Lu, Jingwei, Khanolkar, Nimish
• Application Number:
  US11/559,659
• Publication Number:
  US 20080114750A1
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/3346 using probabilistic model

Abstract

The subject disclosure pertains to systems and methods for facilitating item retrieval and/or ranking. An original ranking of items can be modified and enhanced utilizing a Markov Random Field (MRF) approach based upon item similarity. Item similarity can be measured utilizing a variety of methods. An MRF similarity model can be generated by measuring of similarity between items. An original ranking of items can be obtained, where each document is evaluated independently based upon a query. For example, the original ranking can be obtained using a keyword search. The original ranking can be enhanced based upon similarity of items. For example, items that are deemed to be similar should have similar rankings. The MRF model can be used in conjunction with original rankings to adjust rankings to reflect item relationships.

Claims

1. A system for ordering items, comprising:

a search component that obtains an original ranking of at least a subset of a plurality of items;

a similarity model component that utilizes a Markov Random Field as a representation of relationships among the plurality of items; and

a rank adjustment component that generates an adjusted ranking of at least the subset as a function of the original ranking and the representation.

2. The system of claim 1, further comprising a similarity measure component that determines at least one similarity score for a pair of items, the representation is based at least in part upon the at least one similarity score.

3. The system of claim 2, the at least one similarity score is based at least in part upon a BM-25 model for measuring text-based similarity.

4. The system of claim 2, the at least one similarity score is based at least in part upon semantics of the pair of items.

5. The system of claim 2, the at least one similarity score is based at least in part upon metadata associated with the pair of items.

6. The system of claim 1, further comprising:

a model generator component that subdivides the plurality of items into a plurality of clusters; and

a similarity measure component that determines at least one similarity score for a pair of the clusters, the representation is based at least in part upon of the similarity score.

7. The system of claim 1, further comprising:

a model generator component that classifies the plurality of items into a plurality of categories; and

a similarity measure component that determines at least one similarity score for a pair of the categories, the representation is based at least in part upon of the similarity score.

8. The system of claim 1, the rank adjustment component utilizes a linear program in adjusted ranking generation.

9. The system of claim 8, the rank adjustment component utilizes at least one of a Second Order Cone Program (SOCP) and a quadratic program in adjusted ranking generation.

10. The system of claim 1, further comprising:

a model generator component that identifies at least one item related to a first item; and

a similarity measure component that determines at least one similarity score for the first item and the related item, the representation is based at least in part upon of the similarity score.

11. A method of facilitating item retrieval from a set of items, comprising:

obtaining initial search results of at least for the set of items; and

updating the initial search results as a function of a Markov Random Field modeling similarity of items within the set.

12. The method of claim 11, further comprising:

performing an initial search of the set of items based at least in part upon a query; and

providing the updated results for presentation to a user.

13. The method of claim 11, further comprising:

determining a similarity score for at least one pair of items of the set of items; and

constructing the Markov Random Field model based upon the similarity score.

14. The method of claim 13, the similarity score is based at least in part upon presence of a common term in the item pair.

15. The method of claim 14, the similarity score is based at least in part upon a semantic analysis of the item pair.

16. The method of claim 14, the similarity score is based at least in part metadata associated with the item pair.

17. The method of claim 11, further comprising:

utilizing a clustering algorithm to group the items into a plurality of clusters;

determining a similarity score for at least one pair of clusters; and

constructing the Markov Random Field model based upon the similarity score.

18. The method of claim 11, further comprising:

classifying the items into a plurality of categories;

determining a similarity score for at least one pair of categories; and

constructing the Markov Random Field model based upon the similarity score.

19. A system for ordering a set of items, comprising:

means for receiving an initial ordering of at least a subset of the items; and

means for modifying the initial ordering based at least in part upon a Markov Random Field model of item similarity based at least in part upon text of the items.

20. The system of claim 19, further comprising:

means for measuring the item similarity as a function of item text; and

means for generating a Markov Random Field model utilizing the measurement of item similarity.