Streaming Algorithms for Diversity Maximization with Fairness Constraints

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Abstract

Diversity maximization is a fundamental problem with wide applications in data summarization, web search, and recommender systems. Given a set X of n elements, it asks to select a subset S of k << n elements with maximum diversity, as quantified by the dissimilarities among the elements in S. In this paper, we focus on the diversity maximization problem with fairness constraints in the streaming setting. Specifically, we consider the max-min dispersion diversity objective, which aims to select the subset S that maximizes the minimum distance (dissimilarity) between any pair of distinct elements in S. Assuming that the set X is partitioned into m disjoint groups by some sensitive attribute, e.g., sex or race, ensuring fairness requires that the selected subset S contains k_i elements from each group i in [1,m]. A streaming algorithm should process X sequentially in one pass and return a subset with maximum diversity while guaranteeing the fairness constraint. Although diversity maximization has been extensively studied, the only known algorithms that can work with the max-min diversity objective and fairness constraints are very inefficient for data streams. Since diversity maximization is NP-hard in general, we propose two novel approximation algorithms for fair diversity maximization in data streams, the first of which is (1-ε)/4-approximate and specific for m=2, where ε in (0,1), and the second of which achieves a (1-ε)/(3m+2)-approximation for an arbitrary m. Experimental results show that both algorithms provide solutions of comparable quality to the state-of-the-art algorithms while running several orders of magnitude faster in the streaming setting.
Original languageEnglish
Title of host publication2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022) : ICDE 2022
Number of pages13
PublisherIEEE
Publication date2 Aug 2022
Pages41-53
ISBN (Print)978-1-6654-0884-4
ISBN (Electronic)978-1-6654-0883-7
DOIs
Publication statusPublished - 2 Aug 2022
MoE publication typeA4 Article in conference proceedings
EventIEEE International Conference on Data Engineering - Kuala Lumpur, Malaysia
Duration: 9 May 202212 May 2022
Conference number: 38

Publication series

NameIEEE International Conference on Data Engineering
PublisherIEEE COMPUTER SOC
ISSN (Print)1084-4627

Fields of Science

  • 113 Computer and information sciences
  • algorithmic fairness
  • diversity maximization
  • max-min dispersion
  • streaming algorithm
  • APPROXIMATION ALGORITHMS

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