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- W1793670505 abstract "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 clickthrough 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." @default.
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- W1793670505 date "2015-05-20" @default.
- W1793670505 modified "2023-10-17" @default.
- W1793670505 title "A New Algorithm for Inferring User Search Goals with Feedback Sessions" @default.
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- W1793670505 doi "https://doi.org/10.5120/20812-3099" @default.
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