Personalized recommender systems have become indispensable in today’s online world. Most of today’s recommendation algorithms are data-driven and based on behavioral data. While such systems can produce useful recommendations, they are often uninterpretable, black-box models, which do not incorporate the underlying cognitive reasons for user behavior in the algorithms’ design. The aim of this survey is to present a thorough review of the state of the art of recommender systems that leverage psychological constructs and theories to model and predict user behavior and improve the recommendation process. We call such systems psychology-informed recommender systems. The survey identifies three categories of psychology-informed recommender systems: cognition-inspired, personality-aware, and affectaware recommender systems. Moreover, for each category, we highlight domains, in which psychological theory plays a key role and is therefore considered in the recommendation process. As recommender systems are fundamental tools to support human decision making, we also discuss selected decision-psychological phenomena that impact the interaction between a user and a recommender. Besides, we discuss related work that investigates the evaluation of recommender systems from the user perspective and highlight user-centric evaluation frameworks. We discuss potential research tasks for future work at the end of this survey.

个性化推荐系统在当今的网络世界中变得不可或缺。当今的大多数推荐算法都是数据驱动的，并且基于行为数据。虽然这样的系统可以产生有用的推荐，但它们通常是无法解释的黑盒模型，它们没有在算法设计中包含用户行为的潜在认知原因。本次调查的目的是全面回顾推荐系统的最新进展，这些系统利用心理结构和理论来建模和预测用户行为并改进推荐过程。我们称这种系统为心理知情推荐系统。该调查确定了三类基于心理学的推荐系统：认知启发、个性感知和情感感知推荐系统。此外，对于每个类别，我们强调心理学理论在其中起着关键作用的领域，因此在推荐过程中被考虑在内。由于推荐系统是支持人类决策的基本工具，我们还讨论了影响用户和推荐者之间交互的选定决策心理现象。此外，我们讨论了从用户角度研究推荐系统评估的相关工作，并强调以用户为中心的评估框架。我们在本次调查结束时讨论了未来工作的潜在研究任务。我们还讨论了影响用户和推荐人之间交互的选定决策心理现象。此外，我们讨论了从用户角度研究推荐系统评估的相关工作，并强调以用户为中心的评估框架。我们在本次调查结束时讨论了未来工作的潜在研究任务。我们还讨论了影响用户和推荐人之间交互的选定决策心理现象。此外，我们讨论了从用户角度研究推荐系统评估的相关工作，并强调以用户为中心的评估框架。我们在本次调查结束时讨论了未来工作的潜在研究任务。