Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms

Yanhao Wang, Michael Mathioudakis, Jia Li, Francesco Fabbri

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu


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 (1−ε)/5-approximation algorithm for any ε∈(0,1) that scales to large datasets. Extensive experiments on real-world datasets demonstrate the superior performance of our proposed algorithms over existing ones.
OtsikkoProceedings of the 2023 SIAM International Conference on Data Mining (SDM)
ToimittajatShashi Shekhar, Zhi-Hua Zhou, Yao-Y Chiang, Gregor Stiglic
KustantajaSociety for Industrial and Applied Mathematics
Sivut 91 - 99
ISBN (elektroninen)978-1-61197-765-3
DOI - pysyväislinkit
TilaJulkaistu - 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaSIAM International Conference on Data Mining - Minneapolis, Yhdysvallat (USA)
Kesto: 27 huhtik. 202329 huhtik. 2023


  • 113 Tietojenkäsittely- ja informaatiotieteet

Siteeraa tätä