Fair and Representative Subset Selection from Data Streams

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

We study the problem of extracting a small subset of representative items from a large data stream. In many data mining and machine learning applications such as social network analysis and recommender systems, this problem can be formulated as maximizing a monotone submodular function subject to a cardinality constraint k. In this work, we consider the setting where data items in the stream belong to one of several disjoint groups and investigate the optimization problem with an additional fairness constraint that limits selection to a given number of items from each group. We then propose efficient algorithms for the fairness-aware variant of the streaming submodular maximization problem. In particular, we first give a (1/2-ε)-approximation algorithm that requires O((1/ε) log(k/ε)) passes over the stream for any constant ε>0. Moreover, we give a single-pass streaming algorithm that has the same approximation ratio of (1/2-ε) when unlimited buffer sizes and post-processing time are permitted, and discuss how to adapt it to more practical settings where the buffer sizes are bounded. Finally, we demonstrate the efficiency and effectiveness of our proposed algorithms on two real-world applications, namely maximum coverage on large graphs and personalized recommendation.
Original languageEnglish
Title of host publicationInternational World Wide Web Conference
Publication statusAccepted/In press - 2021
MoE publication typeA4 Article in conference proceedings
EventThe Web Conference 2021 - Ljubljana, Slovenia
Duration: 19 Apr 202123 Apr 2021
https://www2021.thewebconf.org/

Fields of Science

  • 113 Computer and information sciences

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