Fairness-aware group recommendation with pareto-efficiency

Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, Shaoping Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationRecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages107-115
Number of pages9
ISBN (Electronic)9781450346528
DOIs
StatePublished - Aug 27 2017
Event11th ACM Conference on Recommender Systems, RecSys 2017 - Como, Italy
Duration: Aug 27 2017Aug 31 2017

Publication series

NameRecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems

Other

Other11th ACM Conference on Recommender Systems, RecSys 2017
CountryItaly
CityComo
Period8/27/178/31/17

Fingerprint

Semantics
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Information Systems
  • Software

Cite this

Lin, X., Zhang, M., Zhang, Y., Gu, Z., Liu, Y., & Ma, S. (2017). Fairness-aware group recommendation with pareto-efficiency. In RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems (pp. 107-115). (RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems). Association for Computing Machinery, Inc. https://doi.org/10.1145/3109859.3109887
Lin, Xiao ; Zhang, Min ; Zhang, Yongfeng ; Gu, Zhaoquan ; Liu, Yiqun ; Ma, Shaoping. / Fairness-aware group recommendation with pareto-efficiency. RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc, 2017. pp. 107-115 (RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems).
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Lin, X, Zhang, M, Zhang, Y, Gu, Z, Liu, Y & Ma, S 2017, Fairness-aware group recommendation with pareto-efficiency. in RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems. RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems, Association for Computing Machinery, Inc, pp. 107-115, 11th ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, 8/27/17. https://doi.org/10.1145/3109859.3109887

Fairness-aware group recommendation with pareto-efficiency. / Lin, Xiao; Zhang, Min; Zhang, Yongfeng; Gu, Zhaoquan; Liu, Yiqun; Ma, Shaoping.

RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc, 2017. p. 107-115 (RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Lin X, Zhang M, Zhang Y, Gu Z, Liu Y, Ma S. Fairness-aware group recommendation with pareto-efficiency. In RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc. 2017. p. 107-115. (RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems). https://doi.org/10.1145/3109859.3109887