文章基本信息

标题：The Bayesian Optimal Algorithm for Query Refinement in Information Retrieval
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作者：Yasunari Maeda ; Fumitaro Goto ; Hiroshi Masui 等
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2011
卷号：11
期号：10
页码：91-95
出版社：International Journal of Computer Science and Network Security
摘要：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.
关键词：Information retrieval; query refinement; Markov decision processes; Bayes criterion