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.

中文翻译：

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用于个性化推荐的情境感知增强器

当用户与推荐系统（RecSys）交互时，当前情况（例如时间、位置和环境）会显着影响他们的偏好。情境作为交互的背景，用户和项目之间的关系随着情境的变化而变化。然而，现有的 RecSys 将情况、用户和项目视为同一级别。它们只能分别对情境与用户/项目之间的关系进行建模，而不能对情境对用户-项目关联（即用户偏好）的动态影响进行建模。在本文中，我们提供了一种新的视角，将情境作为用户交互的前提。这种视角使我们能够将情况与用户/项目表示分开，并捕获情况对用户-项目关系的影响，从而提供对情况的更全面的理解。基于此，我们提出了一种新颖的情境感知推荐增强器（SARE），这是一个可插入模块，可以将情境集成到各种现有的 RecSys 中。由于用户对情境的感知以及情境对偏好的影响都是个性化的，因此 SARE 包括个性化情境融合 (PSF) 和用户条件偏好编码器 (UCPE)，分别对情境的感知和影响进行建模。我们在两个真实世界数据集的不同设置下的七个主干上进行了应用 SARE 的实验。实验结果表明，与主干网和 SOTA 态势感知基线相比，SARE 显着提高了推荐性能。