research-article

Fairness-Aware Group Recommendation with Pareto-Efficiency

Authors:

Zhang Yongfeng,

Ma ShaopingAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 107 - 115

https://doi.org/10.1145/3109859.3109887

Published: 27 August 2017 Publication History

Abstract

Group recommendation has attracted significant research efforts for its importance in benefiting a group of users. This paper investigates the Group Recommendation problem from a novel aspect, which tries to maximize the satisfaction of each group member while minimizing the unfairness between them. In this work, we present several semantics of the individual utility and propose two concepts of social welfare and fairness for modeling the overall utilities and the balance between group members. We formulate the problem as a multiple objective optimization problem and show that it is NP-Hard in different semantics. Given the multiple-objective nature of fairness-aware group recommendation problem, we provide an optimization framework for fairness-aware group recommendation from the perspective of Pareto Efficiency. We conduct extensive experiments on real-world datasets and evaluate our algorithm in terms of standard accuracy metrics. The results indicate that our algorithm achieves superior performances and considering fairness in group recommendation can enhance the recommendation accuracy.

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Cited By

Carnovalini FRodà AWiggins G(2025)Popularity Bias in Recommender Systems: The Search for Fairness in the Long TailInformation10.3390/info1602015116:2(151)Online publication date: 19-Feb-2025
https://doi.org/10.3390/info16020151
Zhao YWang YLiu YCheng XAggarwal CDerr T(2025)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/366492816:1(1-28)Online publication date: 3-Jan-2025
https://dl.acm.org/doi/10.1145/3664928
Gan DGao MLi WWang ZGuo LJiang FSong Y(2025)LARGEExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125416260:COnline publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125416
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Index Terms

Fairness-Aware Group Recommendation with Pareto-Efficiency
1. Applied computing
  1. Computers in other domains
    1. Computing in government
      1. Voting / election technologies
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Social recommendation

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Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 ACM.

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Publication History

Published: 27 August 2017

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Funding Sources

Natural Science Foundation of China
National Key Basic Research Program
Center for Intelligent Information Retrieval

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

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RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Carnovalini FRodà AWiggins G(2025)Popularity Bias in Recommender Systems: The Search for Fairness in the Long TailInformation10.3390/info1602015116:2(151)Online publication date: 19-Feb-2025
https://doi.org/10.3390/info16020151
Zhao YWang YLiu YCheng XAggarwal CDerr T(2025)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/366492816:1(1-28)Online publication date: 3-Jan-2025
https://dl.acm.org/doi/10.1145/3664928
Gan DGao MLi WWang ZGuo LJiang FSong Y(2025)LARGEExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125416260:COnline publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125416
Xie JYang YWang ZWu LLarson K(2024)Learning fair representations for recommendation via information bottleneck principleProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/273(2469-2477)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/273
Lu HZhang L(2024)The Power of Linear Programming in Sponsored Listings Ranking: Evidence from Field ExperimentsSSRN Electronic Journal10.2139/ssrn.4767661Online publication date: 2024
https://doi.org/10.2139/ssrn.4767661
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
Kraus MKlein SWagner NMinker WAndré E(2024)A Pilot Study on Multi-Party Conversation Strategies for Group RecommendationsProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665569(1-7)Online publication date: 8-Jul-2024
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Tommasel ADiaz-Pace J(2024)Leveraging Monte Carlo Tree Search for Group RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691713(1136-1141)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691713
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
Chee JKalyanaraman SErnala SWeinsberg UDean SIoannidis SBaeza-Yates RBonchi F(2024)Harm Mitigation in Recommender Systems under User Preference DynamicsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671925(255-265)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671925
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