research-article

Enumerating Fair Packages for Group Recommendations

Author:

Ryoma SatoAuthors Info & Claims

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Pages 870 - 878

https://doi.org/10.1145/3488560.3498432

Published: 15 February 2022 Publication History

Abstract

Package-to-group recommender systems recommend a set of unified items to a group of people. Different from conventional settings, it is not easy to measure the utility of group recommendations because it involves more than one user. In particular, fairness is crucial in group recommendations. Even if some members in a group are substantially satisfied with a recommendation, it is undesirable if other members are ignored to increase the total utility. Many methods for evaluating and applying the fairness of group recommendations have been proposed in the literature. However, all these methods maximize the score and output only one package. This is in contrast to conventional recommender systems, which output several (e.g., top-K) candidates. This can be problematic because a group can be dissatisfied with the recommended package owing to some unobserved reasons, even if the score is high. To address this issue, we propose a method to enumerate fair packages efficiently. Our method furthermore supports filtering queries, such as top-K and intersection, to select favorite packages when the list is long. We confirm that our algorithm scales to large datasets and can balance several aspects of the utility of the packages.

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Cited By

Gómez EContreras DBoratto LSalamo M(2024)AMBAR: A dataset for Assessing Multiple Beyond-Accuracy RecommendersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688067(137-147)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688067
Liang Y(2024)FAER: Fairness-Aware Event-Participant Recommendation in Event-Based Social NetworksIEEE Transactions on Big Data10.1109/TBDATA.2024.337240910:5(655-668)Online publication date: Oct-2024
https://doi.org/10.1109/TBDATA.2024.3372409
Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: May-2024
https://doi.org/10.1109/MCI.2024.3363984
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Index Terms

Enumerating Fair Packages for Group Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Information retrieval diversity
      2. Top-k retrieval in databases
    2. Retrieval tasks and goals
      1. Recommender systems
2. Theory of computation
  1. Design and analysis of algorithms
    1. Parameterized complexity and exact algorithms
      1. Fixed parameter tractability

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cover image ACM Conferences

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

February 2022

1690 pages

ISBN:9781450391320

DOI:10.1145/3488560

General Chairs:
K. Selcuk Candan
Arizona State University, USA
,
Huan Liu
Arizona State University, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Xin Luna Dong
Meta Platforms, Inc. (former Facebook), USA
,
Jiliang Tang
Michigan State University, USA

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Published: 15 February 2022

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WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining

February 21 - 25, 2022

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Cited By

Gómez EContreras DBoratto LSalamo M(2024)AMBAR: A dataset for Assessing Multiple Beyond-Accuracy RecommendersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688067(137-147)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688067
Liang Y(2024)FAER: Fairness-Aware Event-Participant Recommendation in Event-Based Social NetworksIEEE Transactions on Big Data10.1109/TBDATA.2024.337240910:5(655-668)Online publication date: Oct-2024
https://doi.org/10.1109/TBDATA.2024.3372409
Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: May-2024
https://doi.org/10.1109/MCI.2024.3363984
Shyam AKumar VKagita VPujari A(2024)UniRecSysKnowledge-Based Systems10.1016/j.knosys.2024.111552289:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111552
Yang QWang YWu ZZhang JLiu LZhang J(2024)Potential factors-embedding group recommendation for online educationDiscover Computing10.1007/s10791-024-09439-427:1Online publication date: 9-May-2024
https://doi.org/10.1007/s10791-024-09439-4
Wang CShi YPeng J(2024)Personalized Group Recommendation Model Based on Hybrid Graph Neural NetworkAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5615-5_37(458-466)Online publication date: 3-Aug-2024
https://doi.org/10.1007/978-981-97-5615-5_37
Li YChen HXu SGe YTan JLiu SZhang Y(2023)Fairness in Recommendation: Foundations, Methods, and ApplicationsACM Transactions on Intelligent Systems and Technology10.1145/361030214:5(1-48)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3610302
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
Liu XYang LLiu ZLi XYang MWang CYu P(2023)Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386885(393-402)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386885
Xu CZhang YChen HDong LWang W(2023)A fairness-aware graph contrastive learning recommender framework for social tagging systemsInformation Sciences: an International Journal10.1016/j.ins.2023.119064640:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119064
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