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Fairness-Aware Explainable Recommendation over Knowledge Graphs

Authors:

Qiaoying Huang,

Yongfeng Zhang,

Gerard de MeloAuthors Info & Claims

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 69 - 78

https://doi.org/10.1145/3397271.3401051

Published: 25 July 2020 Publication History

Abstract

There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. For example, explainable recommendation systems may suffer from both explanation bias and performance disparity. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations due to their insufficient training data, and that their recommendations may be biased by the training records of active users due to the nature of collaborative filtering, which leads to unfair treatment by the system. In this paper, we analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. Empirically, we find that such performance gap is caused by the disparity of data distribution, specifically the knowledge graph path distribution in this work. We propose a fairness constrained approach via heuristic re-ranking to mitigate this unfairness problem in the context of explainable recommendation over knowledge graphs. We experiment on several real-world datasets with state-of-the-art knowledge graph-based explainable recommendation algorithms. The promising results show that our algorithm is not only able to provide high-quality explainable recommendations, but also reduces the recommendation unfairness in several aspects.

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Cited By

Cao YShang SWang JZhang W(2025)Explainable Session-Based Recommendation via Path ReasoningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348632637:1(278-290)Online publication date: Jan-2025
https://doi.org/10.1109/TKDE.2024.3486326
Li JRen YSanderson MDeng K(2024)Explaining Recommendation Fairness from a User/Item PerspectiveACM Transactions on Information Systems10.1145/369887743:1(1-30)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3698877
Rodriguez JTommasel A(2024)Leveraging User History with Transformers for News Clicking: The DArgk ApproachProceedings of the Recommender Systems Challenge 202410.1145/3687151.3687161(48-52)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3687151.3687161
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Fairness-Aware Explainable Recommendation over Knowledge Graphs

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cover image ACM Conferences

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2020

2548 pages

ISBN:9781450380164

DOI:10.1145/3397271

General Chairs:
Jimmy Huang
York University, Canada
,
Yi Chang
Jilin University, China
,
Xueqi Cheng
Chinese Academy of Sciences, China
,
Program Chairs:
Jaap Kamps
University of Amsterdam, Netherlands
,
Vanessa Murdock
Amazon, U.S.A.
,
Ji-Rong Wen
Renmin University of China, China
,
Yiqun Liu
Tsinghua University, China

Copyright © 2020 ACM.

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Published: 25 July 2020

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SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval

July 25 - 30, 2020

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Cited By

Cao YShang SWang JZhang W(2025)Explainable Session-Based Recommendation via Path ReasoningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348632637:1(278-290)Online publication date: Jan-2025
https://doi.org/10.1109/TKDE.2024.3486326
Li JRen YSanderson MDeng K(2024)Explaining Recommendation Fairness from a User/Item PerspectiveACM Transactions on Information Systems10.1145/369887743:1(1-30)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3698877
Rodriguez JTommasel A(2024)Leveraging User History with Transformers for News Clicking: The DArgk ApproachProceedings of the Recommender Systems Challenge 202410.1145/3687151.3687161(48-52)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3687151.3687161
Zhao YWang YLiu YCheng XAggarwal CDerr T(2024)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/366492816:1(1-28)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3664928
Zeng KJin HLv XZhu FHou LZhang YPang FQi YLiu DLi JFeng L(2024)XLORE 3: A Large-Scale Multilingual Knowledge Graph from Heterogeneous Wiki Knowledge ResourcesACM Transactions on Information Systems10.1145/366052142:6(1-47)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3660521
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
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
Wang XLi QYu DLi QXu G(2024)Counterfactual Explanation for Fairness in RecommendationACM Transactions on Information Systems10.1145/364367042:4(1-30)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3643670
Krause TDeriyeva ABeinke JBartels GThomas O(2024)Mitigating Exposure Bias in Recommender Systems—A Comparative Analysis of Discrete Choice ModelsACM Transactions on Recommender Systems10.1145/36412913:2(1-37)Online publication date: 27-Jan-2024
https://dl.acm.org/doi/10.1145/3641291
Liu QFeng XGu TLiu X(2024)FairCRS: Towards User-oriented Fairness in Conversational Recommendation SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688150(126-136)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688150
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