One Size Does Not Fit All: Towards User- and Query-Dependent Ranking For Web Databases

With the emergence of the deep Web databases, searching 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 the results of Web database queries. 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 proposed 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 both analytically and experimentally over two distinct Web databases