When users interact with Recommender Systems (RecSys), current situations, such as time, location, and environment, significantly influence their preferences. Situations serve as the background for interactions, where relationships between users and items evolve with situation changes. However, existing RecSys treat situations, users, and items on the same level. They can only model the relations between situations and users/items respectively, rather than the dynamic impact of situations on user-item associations (i.e., user preferences). In this paper, we provide a new perspective that takes situations as the preconditions for users' interactions. This perspective allows us to separate situations from user/item representations, and capture situations' influences over the user-item relationship, offering a more comprehensive understanding of situations. Based on it, we propose a novel Situation-Aware Recommender Enhancer (SARE), a pluggable module to integrate situations into various existing RecSys. Since users' perception of situations and situations' impact on preferences are both personalized, SARE includes a Personalized Situation Fusion (PSF) and a User-Conditioned Preference Encoder (UCPE) to model the perception and impact of situations, respectively. We conduct experiments of applying SARE on seven backbones in various settings on two real-world datasets. Experimental results indicate that SARE improves the recommendation performances significantly compared with backbones and SOTA situation-aware baselines.

翻译：当用户与推荐系统交互时，当前情境（如时间、地点和环境）会显著影响其偏好。情境作为交互的背景，用户与物品之间的关系会随情境变化而动态演变。然而，现有推荐系统将情境、用户和物品置于同一层级，仅能分别建模情境与用户/物品的关系，而无法捕捉情境对用户-物品关联（即用户偏好）的动态影响。本文提出新视角：将情境视为用户交互的前提条件。该视角使我们能解耦情境与用户/物品表征，并捕获情境对用户-物品关系的影响，从而更全面地理解情境。基于此，我们设计了一种新型情境感知推荐增强器（SARE），这是一个可插拔模块，能将情境集成至各类现有推荐系统。由于用户对情境的感知及情境对偏好的影响均具有个性化特征，SARE包含个性化情境融合模块（PSF）和用户条件偏好编码器（UCPE），分别建模情境的感知过程与影响机制。我们在两个真实数据集上对七个基准模型进行了SARE的多种设置实验。结果表明，与基准模型及现有最优情境感知基线相比，SARE显著提升了推荐性能。