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Adapting Document Ranking to Users' Preferences using Click-through Data

Min Zhao, Hang Li, Adwait Ratnaparkhi, Hsiao-Wuen Hon, and Jue Wang


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.


Publication typeTechReport
InstitutionMicrosoft Research
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