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.
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Ren, X. et al. (2010). Social Relation Based Search Refinement: Let Your Friends Help You!. In: An, A., Lingras, P., Petty, S., Huang, R. (eds) Active Media Technology. AMT 2010. Lecture Notes in Computer Science, vol 6335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15470-6_48
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DOI: https://doi.org/10.1007/978-3-642-15470-6_48
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