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 counter-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 and responsible 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 and responsible 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:建议系统(RS)处于以人为中心的大赦国际的最前沿,在网络的几乎每个角落都广泛部署,为人类决策进程提供便利;然而,尽管其能力和潜力巨大,RS也可能导致对用户、项目、生产商、平台乃至整个社会产生不理想的反效应,例如由于不透明、对不同消费者或生产者的不公平待遇、由于广泛使用用户的私人数据进行个性化而损害用户信任,仅举几个例子,所有这些都造成了对可信赖的咨询系统(TRS)的迫切需要,以减轻或避免这种不利影响和风险;在本调查中,我们将采用与可信和负责任的建议有关的技术,包括但不限于可解释的建议、公平性、隐私意识建议、稳健的建议、用户可控制的建议以及这些不同观点在可信和负责任的建议方面的关系;我们希望通过这次调查,向读者提供对研究领域的全面看法,并提请社区注意关于可靠建议的重要性、现有研究成就和未来研究方向。