Exploring reductions for long web queries

Long queries form a difficult, but increasingly important segment for web search engines. Query reduction, a technique for dropping unnecessary query terms from long queries, improves performance of ad-hoc retrieval on TREC collections. Also, it has great potential for improving long web queries (upto 25% improvement in NDCG@5). However, query reduction on the web is hampered by the lack of accurate query performance predictors and the constraints imposed by search engine architectures and ranking algorithms. In this paper, we present query reduction techniques for long web queries that leverage effective and efficient query performance predictors. We propose three learning formulations that combine these predictors to perform automatic query reduction. These formulations enable trading of average improvements for the number of queries impacted, and enable easy integration into the search engine's architecture for rank-time query reduction. Experiments on a large collection of long queries issued to a commercial search engine show that the proposed techniques significantly outperform baselines, with more than 12% improvement in NDCG@5 in the impacted set of queries. Extension to the formulations such as result interleaving further improves results. We find that the proposed techniques deliver consistent retrieval gains where it matters most: poorly performing long web queries.