@inproceedings{dde77ad1cd764044a9c2ec7b618126a5,
title = "Trustworthy Recommender Systems: Foundations and Frontiers",
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.",
keywords = "accountability, auditability, environmental well-being, explainability, fairness, privacy, recommender systems, robustness",
author = "Wenqi Fan and Xiangyu Zhao and Lin Wang and Xiao Chen and Jingtong Gao and Qidong Liu and Shijie Wang",
note = "Publisher Copyright: {\textcopyright} 2023 Owner/Author.; 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023 ; Conference date: 06-08-2023 Through 10-08-2023",
year = "2023",
month = aug,
day = "4",
doi = "10.1145/3580305.3599575",
language = "英语",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery ",
pages = "5796--5797",
booktitle = "KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
address = "美国",
}