Trustworthy Recommender Systems: Foundations and Frontiers

Wenqi Fan; Xiangyu Zhao; Lin Wang; Xiao Chen; Jingtong Gao; Qidong Liu; Shijie Wang

doi:10.1145/3580305.3599575

Trustworthy Recommender Systems: Foundations and Frontiers

Wenqi Fan, Xiangyu Zhao, Lin Wang, Xiao Chen, Jingtong Gao, Qidong Liu, Shijie Wang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

Recommender systems aim to provide personalized suggestions to users, helping them make effective decisions. However, recent evidence has revealed the untrustworthy aspects of advanced recommender systems, leading to harmful effects in safety-critical areas like finance and healthcare. This tutorial will offer a comprehensive overview of achieving trustworthy recommender systems. It will cover six important aspects: Safety & Robustness, Non-discrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. Each aspect will be defined and categorized, followed by a discussion of the latest research progress and notable works. Additionally, potential interactions among these aspects and future research directions for trustworthy recommender systems will be explored.

Original language	English
Title of host publication	KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	5796-5797
Number of pages	2
ISBN (Electronic)	9798400701030
DOIs	https://doi.org/10.1145/3580305.3599575
Publication status	Published - 6 Aug 2023
Event	29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023 - Long Beach, United States Duration: 6 Aug 2023 → 10 Aug 2023

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023
Country/Territory	United States
City	Long Beach
Period	6/08/23 → 10/08/23

Keywords

accountability
auditability
environmental well-being
explainability
fairness
privacy
recommender systems
robustness

ASJC Scopus subject areas

Software
Information Systems

More information

10.1145/3580305.3599575

Cite this

Fan, W., Zhao, X., Wang, L., Chen, X., Gao, J., Liu, Q., & Wang, S. (2023). Trustworthy Recommender Systems: Foundations and Frontiers. In KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5796-5797). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3580305.3599575

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abstract = "Recommender systems aim to provide personalized suggestions to users, helping them make effective decisions. However, recent evidence has revealed the untrustworthy aspects of advanced recommender systems, leading to harmful effects in safety-critical areas like finance and healthcare. This tutorial will offer a comprehensive overview of achieving trustworthy recommender systems. It will cover six important aspects: Safety & Robustness, Non-discrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. Each aspect will be defined and categorized, followed by a discussion of the latest research progress and notable works. Additionally, potential interactions among these aspects and future research directions for trustworthy recommender systems will be explored.",

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author = "Wenqi Fan and Xiangyu Zhao and Lin Wang and Xiao Chen and Jingtong Gao and Qidong Liu and Shijie Wang",

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Fan, W, Zhao, X, Wang, L, Chen, X, Gao, J, Liu, Q & Wang, S 2023, Trustworthy Recommender Systems: Foundations and Frontiers. in KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 5796-5797, 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, United States, 6/08/23. https://doi.org/10.1145/3580305.3599575

Trustworthy Recommender Systems: Foundations and Frontiers. / Fan, Wenqi; Zhao, Xiangyu; Wang, Lin et al.
KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2023. p. 5796-5797 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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Fan W, Zhao X, Wang L, Chen X, Gao J, Liu Q et al. Trustworthy Recommender Systems: Foundations and Frontiers. In KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2023. p. 5796-5797. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3580305.3599575

Trustworthy Recommender Systems: Foundations and Frontiers

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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