research-article

Top-N group recommendations with fairness

Author:

Dimitris SacharidisAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 1663 - 1670

https://doi.org/10.1145/3297280.3297442

Published: 08 April 2019 Publication History

Abstract

In many settings it is required that items are recommended to a group of users instead of a single user. Conventionally, the objective is to maximize the overall satisfaction among group members. Recently, however, attention has shifted to ensuring that recommendations are fair in that they should minimize the feeling of dissatisfaction among members. In this work, we explore a simple but intuitive notion of fairness: the minimum utility a group member receives. We propose a technique that seeks to rank the Pareto, or unanimously, optimal items by considering all admissible ways in which a group might reach a decision. As our detailed experimental study shows, this results in top-N recommendations that not only achieve a high minimum utility compared to other fairness-aware techniques, but also a high average utility across all group members beating standard aggregation strategies.

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Cited By

Yang HWu XQiu ZZheng YChen X(2024)Distributional Fairness-aware RecommendationACM Transactions on Information Systems10.1145/365285442:5(1-28)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3652854
Tommasel A(2024)Fairness Matters: A look at LLM-generated group recommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688182(993-998)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688182
Xu CXu JDing YZhang XQi QChua TNgo CKa-Wei Lee RKumar RLauw H(2024)FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation RetrievalProceedings of the ACM Web Conference 202410.1145/3589334.3645413(1092-1102)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645413
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Index Terms

Top-N group recommendations with fairness
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Copyright © 2019 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 08 April 2019

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SAC '19

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SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2019

Limassol, Cyprus

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

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Cited By

Yang HWu XQiu ZZheng YChen X(2024)Distributional Fairness-aware RecommendationACM Transactions on Information Systems10.1145/365285442:5(1-28)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3652854
Tommasel A(2024)Fairness Matters: A look at LLM-generated group recommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688182(993-998)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688182
Xu CXu JDing YZhang XQi QChua TNgo CKa-Wei Lee RKumar RLauw H(2024)FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation RetrievalProceedings of the ACM Web Conference 202410.1145/3589334.3645413(1092-1102)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645413
Peng ZZhang HMin F(2024)IUG-CFExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121887238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121887
Stratigi M(2024)Sequential Group Recommendations with Responsibility ConstraintsWeb Engineering10.1007/978-3-031-62362-2_42(448-452)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-62362-2_42
Sacharidis D(2024)Fairness and Explainability for Enabling Trust in AI SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_3(85-110)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_3
TSUKUDA KISHIDA KHAMASAKI MGOTO M(2023)Kiite Cafe: A Web Service Enabling Users to Listen to the Same Song at the Same Moment While Reacting to the SongIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7001E106.D:11(1906-1915)Online publication date: 1-Nov-2023
https://doi.org/10.1587/transinf.2023EDP7001
Wu YCao JXu G(2023)Fairness in Recommender Systems: Evaluation Approaches and Assurance StrategiesACM Transactions on Knowledge Discovery from Data10.1145/360455818:1(1-37)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3604558
Wang YMa WZhang MLiu YMa S(2023)A Survey on the Fairness of Recommender SystemsACM Transactions on Information Systems10.1145/354733341:3(1-43)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3547333
Xu CChen SXu JShen WZhang XWang GDong Z(2023)P-MMF: Provider Max-min Fairness Re-ranking in Recommender SystemProceedings of the ACM Web Conference 202310.1145/3543507.3583296(3701-3711)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583296
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