Query Refinement for Diverse Top-k Selection

Database queries are often used to select and rank items as decision supportfor many applications. As automated decision-making tools become moreprevalent, there is a growing recognition of the need to diversify theiroutcomes. In this paper, we define and study the problem of modifying theselection conditions of an ORDER BY query so that the result of the modifiedquery closely fits some user-defined notion of diversity while simultaneouslymaintaining the intent of the original query. We show the hardness of thisproblem and propose a Mixed Integer Linear Programming (MILP) based solution.We further present optimizations designed to enhance the scalability andapplicability of the solution in real-life scenarios. We investigate theperformance characteristics of our algorithm and show its efficiency and theusefulness of our optimizations.