Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Pruned query evaluation using pre-computed impacts
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A comparison of statistical significance tests for information retrieval evaluation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Pairwise document similarity in large collections with MapReduce
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Efficient processing of complex features for information retrieval
Efficient processing of complex features for information retrieval
Brute force and indexed approaches to pairwise document similarity comparisons with MapReduce
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Efficiency optimizations for interpolating subqueries
Proceedings of the 20th ACM international conference on Information and knowledge management
Probabilistic co-relevance for query-sensitive similarity measurement in information retrieval
Information Processing and Management: an International Journal
An incremental approach to efficient pseudo-relevance feedback
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Pseudo-relevance feedback (PRF) improves search quality by expanding the query using terms from high-ranking documents from an initial retrieval. Although PRF can often result in large gains in effectiveness, running two queries is time consuming, limiting its applicability. We describe a PRF method that uses corpus pre-processing to achieve query-time speeds that are near those of the original queries. Specifically, Relevance Modeling, a language modeling based PRF method, can be recast to benefit substantially from finding pairwise document relationships in advance. Using the resulting Fast Relevance Model (fastRM), we substantially reduce the online retrieval time and still benefit from expansion. We further explore methods for reducing the preprocessing time and storage requirements of the approach, allowing us to achieve up to a 10% increase in MAP over unexpanded retrieval,vwhile only requiring 1% of the time of standard expansion.