Ranking with uncertain scoring functions: semantics and sensitivity measures

Ranking queries report the top-K results according to a user-defined scoring function. A widely used scoring function is the weighted summation of multiple scores. Often times, users cannot precisely specify the weights in such functions in order to produce the preferred order of results. Adopting uncertain/incomplete scoring functions (e.g., using weight ranges and partially-specified weight preferences) can better capture user's preferences in this scenario. In this paper, we study two aspects in uncertain scoring functions. The first aspect is the semantics of ranking queries, and the second aspect is the sensitivity of computed results to refinements made by the user. We formalize and solve multiple problems under both aspects, and present novel techniques that compute query results efficiently to comply with the interactive nature of these problems.