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- W4313679524 abstract "Diversity maximization aims to select a diverse and representative subset of items from a large dataset. It is a fundamental optimization task that finds applications in data summarization, feature selection, web search, recommender systems, and elsewhere. However, in a setting where data items are associated with different groups according to sensitive attributes like sex or race, it is possible that algorithmic solutions for this task, if left unchecked, will under- or over-represent some of the groups. Therefore, we are motivated to address the problem of emph{max-min diversification with fairness constraints}, aiming to select $k$ items to maximize the minimum distance between any pair of selected items while ensuring that the number of items selected from each group falls within predefined lower and upper bounds. In this work, we propose an exact algorithm based on integer linear programming that is suitable for small datasets as well as a $frac{1-varepsilon}{5}$-approximation algorithm for any $varepsilon in (0, 1)$ that scales to large datasets. Extensive experiments on real-world datasets demonstrate the superior performance of our proposed algorithms over existing ones." @default.
- W4313679524 created "2023-01-08" @default.
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- W4313679524 date "2023-01-05" @default.
- W4313679524 modified "2023-09-30" @default.
- W4313679524 title "Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms" @default.
- W4313679524 doi "https://doi.org/10.48550/arxiv.2301.02053" @default.
- W4313679524 hasPublicationYear "2023" @default.
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