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A New Algorithm for Inferring User Search Goals with Feedback Sessions

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5 Author(s)
Zheng Lu ; Dept. of Electron. Eng., Shanghai Jiao Tong Univ., Shanghai, China ; Hongyuan Zha ; Xiaokang Yang ; Weiyao Lin
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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.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:25 ,  Issue: 3 )

Date of Publication:

March 2013

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