Year: 2020
Type: preprint
Abstract: Diversity is an important principle in data selection and summarization, facility location, and recommendation systems. Our work focuses on maximizing diversity in data selection, while offering fairness guarantees. In particular, we offer the first study that augments the Max-Min diversification objective with fairness constraints. More specifically, given a universe $U$ of $n$ elements that can be partitioned into $m$ disjoint groups, we aim to retrieve a $k$-sized subset that maximizes the pairwise minimum distance within the set (diversity) and contains a pre-specified $k_i$ number of elements from each group $i$ (fairness). We show that this problem is NP-complete even in metric spaces, and we propose three novel algorithms, linear in $n$, that provide strong theoretical approximation guarantees for different values of $m$ and $k$. Finally, we extend our algorithms and analysis to the case where groups can be overlapping.
Source: arXiv (Cornell University)
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W3093481237