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A New Algorithm for Inferring User Search Goals with Feedback Sessions
March 2013 (vol. 25 no. 3)
pp. 502-513
Zheng Lu, Shanghai Jiao Tong University, Shanghai
Hongyuan Zha, Georgia Institute of Technology, Atlanta
Xiaokang Yang, Shanghai Jiao Tong University, Shanghai
Weiyao Lin, Shanghai Jiao Tong University, Shanghai
Zhaohui Zheng, Yahoo! Labs, Beijing
For a broad-topic and ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. In this paper, we propose a novel approach to infer user search goals by analyzing search engine query logs. First, we propose a framework to discover different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are constructed from user click-through logs and can efficiently reflect the information needs of users. Second, we propose a novel approach to generate pseudo-documents to better represent the feedback sessions for clustering. Finally, we propose a new criterion )“Classified Average Precision (CAP)” to evaluate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to validate the effectiveness of our proposed methods.
Index Terms:
Search engines,Web search,Search methods,Feedback,Optimization methods,Search problems,Information retrieval,classified average precision,User search goals,feedback sessions,pseudo-documents,restructuring search results
Citation:
Zheng Lu, Hongyuan Zha, Xiaokang Yang, Weiyao Lin, Zhaohui Zheng, "A New Algorithm for Inferring User Search Goals with Feedback Sessions," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 3, pp. 502-513, March 2013, doi:10.1109/TKDE.2011.248
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