Query Chains: Learning to Rank from Implicit Feedback

Details Query Chains: Learning to Rank from Implicit Feedback Query Chains: Learning to Rank from Implicit Feedback

2006-05-08

arxiv.org/abs/cs/0605035v1

Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2005

Computer Science

Learning

10 pages

Scientific paper

This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.

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