Query Refinement for Diverse Top-k Selection
arxiv(2024)
摘要
Database queries are often used to select and rank items as decision support
for many applications. As automated decision-making tools become more
prevalent, there is a growing recognition of the need to diversify their
outcomes. In this paper, we define and study the problem of modifying the
selection conditions of an ORDER BY query so that the result of the modified
query closely fits some user-defined notion of diversity while simultaneously
maintaining the intent of the original query. We show the hardness of this
problem and propose a Mixed Integer Linear Programming (MILP) based solution.
We further present optimizations designed to enhance the scalability and
applicability of the solution in real-life scenarios. We investigate the
performance characteristics of our algorithm and show its efficiency and the
usefulness of our optimizations.
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