Query Chains: Learning to Rank from Implicit Feedback

TL;DR

This paper introduces a method that leverages user query chains and clickthrough data to learn improved ranking functions for web search, demonstrating significant performance gains over traditional static rankings.

Contribution

It proposes a novel use of query chains to generate preference judgments from implicit feedback, enhancing ranking models with user reformulation insights.

Findings

Significant improvement in search result rankings.

Query chain-based preferences outperform non-chain methods.

Real-world implementation shows practical effectiveness.

Abstract

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.