Recommender Systems (RS) have significantly advanced online content filtering and personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS). Despite substantial progress on TRS, most efforts focus on data correlations while overlooking the fundamental causal nature of recommendations. This drawback hinders TRS from identifying the root cause of trustworthiness issues, leading to limited fairness, robustness, and explainability. To bridge this gap, causal learning emerges as a class of promising methods to augment TRS. These methods, grounded in reliable causality, excel in mitigating various biases and noise while offering insightful explanations for TRS. However, there is a lack of timely and dedicated surveys in this vibrant area. This paper creates an overview of TRS from the perspective of causal learning. We begin by presenting the advantages and common procedures of Causality-oriented TRS (CTRS). Then, we identify potential trustworthiness challenges at each stage and link them to viable causal solutions, followed by a classification of CTRS methods. Finally, we discuss several future directions for advancing this field.

翻译：推荐系统（RS）在在线内容过滤与个性化决策方面取得了显著进展。然而，RS中不断涌现的脆弱性正推动着研究范式向可信推荐系统（TRS）转变。尽管TRS研究已取得长足进步，但现有工作大多聚焦于数据相关性，而忽视了推荐行为内在的因果本质。这一缺陷阻碍了TRS追溯可信度问题的根本成因，导致其在公平性、鲁棒性与可解释性方面存在局限。为弥合此鸿沟，因果学习作为一类具有前景的方法应运而生，为增强TRS提供了新路径。这类方法基于可靠的因果机制，在有效缓解各类偏差与噪声的同时，能为TRS提供深刻的机理解释。然而，这一活跃领域目前尚缺乏及时且系统的综述研究。本文从因果学习的视角对TRS研究进行系统性梳理。我们首先阐述因果导向的可信推荐系统（CTRS）的优势与通用流程；继而剖析各阶段潜在的可信性挑战，并将其与可行的因果解决方案相衔接；随后对现有CTRS方法进行分类归纳；最后探讨该领域未来发展的若干方向。