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The Bayesian Optimal Algorithm for Query Refinement in Information Retrieval


Yasunari Maeda, Fumitaro Goto, Hiroshi Masui, Fumito Masui, Masakiyo Suzuki


Vol. 11  No. 10  pp. 91-95


To realize more efficient information retrieval it is critical to improve the user’s original query, because novice users can not be expected to formulate precise and effective queries. Queries can often be improved by adding extra terms that appear in relevant documents but which were not included in the original query. This is called query expansion. Query refinement, a variant of query expansion, interactively recommends new terms related to the original query. Because previous research did not offer any criterion to guarantee optimality, this paper proposes an optimal algorithm for query refinement with reference to the Bayes criterion.


Information retrieval, query refinement, Markov decision processes, Bayes criterion