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Issue No. 09 - Sept. (2012 vol. 24)
ISSN: 1041-4347
pp: 1671-1685
Aditya Telang , University of Texas at Arlington, Arlington
Chengkai Li , University of Texas at Arlington, Arlington
Sharma Chakravarthy , University of Texas at Arlington, Arlington
With the emergence of the deep web, searching web databases in domains such as vehicles, real estate, etc., has become a routine task. One of the problems in this context is ranking the results of a user query. Earlier approaches for addressing this problem have used frequencies of database values, query logs, and user profiles. A common thread in most of these approaches is that ranking is done in a user- and/or query-independent manner. This paper proposes a novel query- and user-dependent approach for ranking query results in web databases. We present a ranking model, based on two complementary notions of user and query similarity, to derive a ranking function for a given user query. This function is acquired from a sparse workload comprising of several such ranking functions derived for various user-query pairs. The model is based on the intuition that similar users display comparable ranking preferences over the results of similar queries. We define these similarities formally in alternative ways and discuss their effectiveness analytically and experimentally over two distinct web databases.
Databases, Mathematical model, Vehicles, Context, Equations, Image color analysis, Color, workload, Automated ranking, web databases, user similarity, query similarity

C. Li, A. Telang and S. Chakravarthy, "One Size Does Not Fit All: Toward User- and Query-Dependent Ranking for Web Databases," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 1671-1685, 2011.
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