Diversifying search results through pattern-based subtopic modeling
Publication in refereed journal


摘要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. Copyright © 2012, IGI Global.
著者Zheng W., Fang H., Cheng H., Wang X.
期刊名稱International Journal on Semantic Web and Information Systems
出版社Idea Group Publishing
出版地United States
頁次37 - 56
關鍵詞Clustering, Diversity, Frequent pattern mining, Information retrieval, Subtopics

上次更新時間 2021-13-06 於 00:35