Query Refinement for Diverse Top-$k$ Selection
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916179340165120 |
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| author | Campbell, Felix S. Silberstein, Alon Stoyanovich, Julia Moskovitch, Yuval |
| author_facet | Campbell, Felix S. Silberstein, Alon Stoyanovich, Julia Moskovitch, Yuval |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17786 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Query Refinement for Diverse Top-$k$ Selection Campbell, Felix S. Silberstein, Alon Stoyanovich, Julia Moskovitch, Yuval Databases 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. |
| title | Query Refinement for Diverse Top-$k$ Selection |
| topic | Databases |
| url | https://arxiv.org/abs/2403.17786 |