Bienvenido Vélez, Ron Wiess, Mark A. Sheldon, and David K. Gifford
Proceedings of the 20th ACM Conference on Research and Development in Information Retrieval (SIGIR 97) , Philadelphia, Pennsylvania, July 1997

Query Refinement is an essential information retrieval tool that interactively recommends new terms related to a particular query. This paper introduces concept recall, an experimental measure of an algorithm's ability to suggest terms humans have judged to be semantically related to an information need. This study uses precision improvement experiments to measure the ability of an algorithm to produce single term query modifications that predict a user's information need as partially encoded by the query. An oracle algorithm produces ideal query modifications, providing a meaningful context for interpreting precision improvement results.

This study also introduces RMAP, a fast and practical query refinement algorithm that refines multiple term queries by dynamically combining precomputed suggestions for single term queries. RMAP achieves accuracy comparable to a much slower algorithm, although both RMAP and the slower algorithm lag behind the best possible term suggestions offered by the oracle. We believe RMAP is fast enough to be integrated into present day Internet search engines: RMAP computes 100 term suggestions for a 160,000 document collection in 15 ms on a low-end PC.

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