With the emergence of the deep Web databases, searching in domains like vehicles, property, etc. has become a routine task. One of the problems during this context is ranking the results of a user query. Earlier approaches for addressing this problem have used frequencies of database values, question logs, and user profiles. A common thread in most of these approaches is that ranking is finished during a user- and/or query-freelance manner. This paper proposes a completely unique question- and user-dependent approach for ranking the results of Net database queries. We present a ranking model, primarily based on 2 complementary notions of user and query similarity, to derive a ranking operate for a given user query. This function is acquired from a sparse workload comprising of many such ranking functions derived for various user-question pairs. The proposed model is predicated on the intuition that similar users display comparable ranking preferences over the results of comparable queries. We outline these similarities formally in alternative ways in which and discuss their effectiveness each analytically and experimentally over two distinct Internet databases.
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