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Optimizing top-k retrieval: submodularity analysis and search strategies 被引量:1
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作者 Chaofeng SHA Keqiang WANG +2 位作者 Dell ZHANG Xiaoling WANG Aoying ZHOU 《Frontiers of Computer Science》 SCIE EI CSCD 2016年第3期477-487,共11页
The key issue in top-k retrieval, finding a set of k documents (from a large document collection) that can best answer a user's query, is to strike the optimal balance between relevance and diversity. In this paper... The key issue in top-k retrieval, finding a set of k documents (from a large document collection) that can best answer a user's query, is to strike the optimal balance between relevance and diversity. In this paper, we study the top-k re- trieval problem in the framework of facility location analysis and prove he submodularity of that objective function which provides a theoretical approximation guarantee of factor 1 -1/ε for the (best-first) greedy search algorithm. Furthermore, we propose a two-stage hybrid search strategy which first ob- tains a high-quality initial set of top-k documents via greedy search, and then refines that result set iteratively via local search. Experiments on two large TREC benchmark datasets show that our two-stage hybrid search strategy approach can supersede the existing ones effectively and efficiently. 展开更多
关键词 top-k retrieval diversification submodular function maximization
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