One Size Does Not Fit All: Towards User & Query Dependent Ranking For Web Databases
نویسندگان
چکیده
In this paper, we propose an automated solution for ranking query results of Web databases in an userand query-dependent environment. We first propose a learning method for inferring a workload of ranking functions by investigating users’ browsing choices over individual query results. Based on this workload, we propose a similarity model, based on two novel metrics – userand querysimilarity, for ranking query results when user browsing choices are not available. We present the results of an experimental study that validates our proposal for userand query-dependent ranking.
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تاریخ انتشار 2009