Social relation based search refinement: let your friends help you!

Authors:
Xu Ren;Yi Zeng;Yulin Qin;Ning Zhong;Zhisheng Huang;Yan Wang;Cong Wang
Affiliations:
International WIC Institute, Beijing University of Technology, Beijing, P.R. China;International WIC Institute, Beijing University of Technology, Beijing, P.R. China;International WIC Institute, Beijing University of Technology, Beijing, P.R. China and Department of Psychology, Carnegie Mellon University, Pittsburgh, PA;International WIC Institute, Beijing University of Technology, Beijing, P.R. China and Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi-City, Japan;Department of Artificial Intelligence, Vrije University Amsterdam, Amsterdam, The Netherlands;International WIC Institute, Beijing University of Technology, Beijing, P.R. China;International WIC Institute, Beijing University of Technology, Beijing, P.R. China
Venue:
AMT'10 Proceedings of the 6th international conference on Active media technology
Year:
2010

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Abstract

One of the major problems for search at Web scale is that the search results on the large scale data might be huge and the users have to browse to find the most relevant ones. Plus, due to the reason for the context, user requirement may diverse although the input query may be the same. In this paper, we try to achieve scalability for Web search through social relation diversity of different users. Namely, we utilize one of the major context for users, social relations, to help refining the search process. Social network based group interest models are developed according to collaborative networks, and is designed to be used in more wider range of Web scale search tasks. The experiments are based on the SwetoDBLP dataset, and we can conclude that proposed method is potentially effective to help users find most relevant search results in the Web environment.