A Survey on Trustworthy Recommender Systems
Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan, Xu, Yunqi Li, Yikun Xian, Yongfeng Zhang

TL;DR
This survey reviews the current state of trustworthy recommender systems, highlighting techniques like explainability, fairness, privacy, and robustness, and discusses future research directions to address ethical and societal challenges.
Contribution
It provides a comprehensive overview of techniques and research progress in trustworthy recommender systems, emphasizing their importance and future challenges.
Findings
Summarizes key techniques for trustworthy recommendation
Highlights the relationship between different trustworthiness perspectives
Identifies future research directions in the field
Abstract
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired effects on users, items, producers, platforms, or even the society at large, such as compromised user trust due to non-transparency, unfair treatment of different consumers, or producers, privacy concerns due to extensive use of user's private data for personalization, just to name a few. All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks. In this survey, we will introduce techniques related to trustworthy recommendation, including but not limited to explainable recommendation, fairness in recommendation, privacy-aware recommendation,…
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Taxonomy
TopicsRecommender Systems and Techniques · Cloud Computing and Resource Management · Spam and Phishing Detection
