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.

翻译：推荐系统（RS）作为人本人工智能的前沿领域，已广泛部署于互联网的各个角落，并促进人类的决策过程。然而，尽管其具备强大的能力与潜力，推荐系统也可能对用户、物品、生产者、平台乃至整个社会产生不良影响，例如因不透明性导致用户信任受损、对不同消费者或生产者的不公平对待、以及因广泛使用用户隐私数据实现个性化推荐引发的隐私问题等。所有这些均迫切需求构建可信推荐系统（TRS），以缓解或避免此类负面影响与风险。本综述将介绍与可信推荐相关的技术，包括但不限于可解释推荐、推荐公平性、隐私感知推荐、推荐鲁棒性、用户可控推荐，以及这些不同视角在可信推荐中的相互关系。通过本综述，我们希望为读者提供该研究领域的全面视角，并引起学界对可信推荐重要性的关注，梳理现有研究成果及未来研究方向。