The field of recommender systems (RS) is currently undergoing two profound paradigm shifts. From the perspective of objectives, the goal has shifted beyond mere recommendation accuracy to comprehensive trustworthiness, encompassing multiple dimensions such as robustness, fairness, and privacy preservation. From a technical perspective, Large Language Models (LLMs) have been extensively integrated into RS, reshaping the foundations of recommendation through richer semantic understanding, stronger intent reasoning, and more flexible user interactions. The convergence of these two shifts prompts a timely and pivotal question: how does the integration of LLMs reshape the landscape of trustworthy recommendation? In this work, we present a systematic review of trustworthy LLM-empowered recommendation. By comprehensively analyzing over 200 recent studies, we reveal that the introduction of LLMs acts as a double-edged sword. While their advanced mechanisms and user-friendly interfaces offer unprecedented opportunities to enhance trustworthiness, they simultaneously introduce new risks, such as novel forms of bias and hallucination-induced issues. To characterize this dual impact, we systematically identify 13 opportunities and 18 challenges across six fundamental dimensions of trustworthiness, and accordingly organize the existing literature into a novel taxonomy. We also provide a comprehensive review of commonly used datasets and evaluation metrics to facilitate empirical validation. Finally, we identify critical open challenges and outline future directions, hoping to inspire future research on this emerging topic.

翻译：推荐系统领域正经历两大深刻范式变革。从目标维度看，其追求已从单纯推荐准确性拓展至涵盖鲁棒性、公平性、隐私保护等多维度的综合可信性；从技术维度看，大语言模型已深度融入推荐系统，通过更丰富的语义理解、更强的意图推理能力及更灵活的用户交互方式重塑推荐基础。这两大变革的融合催生了一个关键而及时的问题：大语言模型的引入如何重塑可信推荐格局？本文对大语言模型赋能的推荐系统进行了系统性综述。通过分析200余篇前沿研究，我们揭示了大语言模型的双刃剑效应：一方面，其先进机制与友好交互界面为增强可信性带来前所未有的机遇；另一方面也引入新型偏见、幻觉诱导等新风险。为刻画这种双重影响，我们在可信性六大基础维度上系统识别出13项机遇与18项挑战，并据此构建了新颖的文献分类体系。同时全面综述了常用数据集与评估指标以促进实证验证。最后，我们识别关键开放挑战并展望未来方向，以期启迪该新兴领域的后续研究。