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ABSTRACT
Web search engines help users find useful information on the World Wide Web (WWW). However, when the same query is submitted by different users, typical search engines return the same result regardless of who submitted the query. Generally, each user has different information needs for his/her query. Therefore, the search result should be adapted to users with different information needs. In this paper, we first propose several approaches to adapting search results according to each user's need for relevant information without any user effort, and then verify the effectiveness of our proposed approaches. Experimental results show that search systems that adapt to each user's preferences can be achieved by constructing user profiles based on modified collaborative filtering with detailed analysis of user's browsing history in one day.
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CITED BY 65
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Jian-Tao Sun , Hua-Jun Zeng , Huan Liu , Yuchang Lu , Zheng Chen, CubeSVD: a novel approach to personalized Web search, Proceedings of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan
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Christos Makris , Yannis Panagis , Yannis Plegas , Evangelos Sakkopoulos, An integrated web system to facilitate personalized web searching algorithms, Proceedings of the 2008 ACM symposium on Applied computing, March 16-20, 2008, Fortaleza, Ceara, Brazil
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Mingfang Wu , James A. Thom , Andrew Turpin , Ross Wilkinson, Cost and benefit analysis of mediated enterprise search, Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries, June 15-19, 2009, Austin, TX, USA
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REVIEW
"Donald Harris Kraft : Reviewer"
This paper deserves a broader audience than just the conference participants who first saw it. The authors consider Web search, for example, information retrieval on the Web, using user profiles. What makes this unique is their attempt to model th
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