Diversifying Search Results through Pattern-Based Subtopic Modeling

Wei Zheng, Hui Fang, Hong Cheng and Xuanhui Wang
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Wei Zheng: Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA
Hui Fang: Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA
Hong Cheng: Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong
Xuanhui Wang: Facebook, Menlo Park, CA, USA

International Journal on Semantic Web and Information Systems (IJSWIS), 2012, vol. 8, issue 4, 37-56

Abstract: Traditional information retrieval models do not necessarily provide users with optimal search experience because the top ranked documents may contain excessively redundant information. Therefore, satisfying search results should be not only relevant to the query but also diversified to cover different subtopics of the query. In this paper, the authors propose a novel pattern-based framework to diversify search results, where each pattern is a set of semantically related terms covering the same subtopic. They first apply a maximal frequent pattern mining algorithm to extract the patterns from retrieval results of the query. The authors then propose to model a subtopic with either a single pattern or a group of similar patterns. A profile-based clustering method is adapted to group similar patterns based on their context information. The search results are then diversified using the extracted subtopics. Experimental results show that the proposed pattern-based methods are effective to diversify the search results.

Date: 2012
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