Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. For a quite long period of time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, system bias. As a result, it has become clear that a strict focus on RS accuracy is limited and the research must consider other important factors, e.g., trustworthiness. For end users, a trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased and fair as well as robust to noise or attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs. To this end, in this paper, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.

翻译：推荐系统（RSs）旨在帮助用户从海量候选项中有效检索其感兴趣的项目。在相当长的一段时间内，研究人员和从业者一直专注于开发高准确度的推荐系统。然而近年来，推荐系统面临越来越多的威胁，包括攻击、系统和用户生成的噪声以及系统偏差。因此，人们逐渐认识到，仅关注推荐系统准确性存在局限性，研究必须考虑其他重要因素，例如可信度。对于最终用户而言，可信推荐系统（TRS）不仅应具备准确性，还应具有透明性、无偏性、公平性，以及对噪声或攻击的鲁棒性。这些观察实际上推动了推荐系统研究的范式转变：从以准确性为导向的推荐系统转向可信推荐系统。然而，在这个新兴且快速发展的可信推荐系统领域，研究者们尚缺乏对其文献的系统性梳理与讨论。为此，本文对可信推荐系统进行了综述，包括讨论其动机与基本概念、阐释构建可信推荐系统面临的挑战，并展望该领域的未来发展方向。我们还提出了一种新颖的概念框架，以支持可信推荐系统的构建。