A Survey on Trustworthy Recommender Systems

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, robustness in recommendation, user-controllable recommendation, as well as the relationship between these different perspectives in terms of trustworthy recommendation. Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research directions on trustworthy recommendation.