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Adapting Document Ranking to Users’
Preferences using Click-through Data
February 1, 2006
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Download Document

BibTex
Authors

Min Zhao

Hang Li

Adwait Ratnaparkhi

Hsiao-Wuen Hon

Jue Wang
Publication Type
TechReport
Pages
10
Number
MSR-TR-2006-15

Abstract

Related Info
Abstract
This paper proposes a new approach for using click-through data to re-rank the documents
retrieved by a search engine. The goal is to make the final ranked list of documents accurately
represent users’ preferences reflected in the click-through data. Our approach combines the
ranking result of a traditional IR algorithm (BM25) with that given by a machine learning
algorithm (Naïve Bayes). The machine learning algorithm is trained on click-through data
(queries and their associated documents), while the IR algorithm runs over the document
collection. We consider several alternative strategies for combining the use of click-through data
and that of document data. Experimental results confirm that any method of using click-through
data greatly improves the preference ranking, over the method of using BM25 alone. We found
that a linear combination of scores of Naïve Bayes and scores of BM25 performs the best for
the task. At the same time, we found that the preference ranking methods can preserve relevance
ranking, i.e., the preference ranking methods can perform as well as BM25 for relevance
ranking.
Related Info
Research Areas
 Data visualization, analytics, and platform
 Search and information retrieval
Research Labs
 Microsoft Research Lab - Asia
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