Streaming Algorithms for Diversity Maximization with Fairness Constraints

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Sammanfattning

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.
Originalspråkengelska
Titel på värdpublikation2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022) : ICDE 2022
Antal sidor13
FörlagIEEE
Utgivningsdatum2 aug. 2022
Sidor41-53
ISBN (tryckt)978-1-6654-0884-4
ISBN (elektroniskt)978-1-6654-0883-7
DOI
StatusPublicerad - 2 aug. 2022
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangIEEE International Conference on Data Engineering - Kuala Lumpur, Malaysia
Varaktighet: 9 maj 202212 maj 2022
Konferensnummer: 38

Publikationsserier

NamnIEEE International Conference on Data Engineering
FörlagIEEE COMPUTER SOC
ISSN (tryckt)1084-4627

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  • 113 Data- och informationsvetenskap

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