A Situation-aware Enhancer for Personalized Recommendation
arxiv(2024)
摘要
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.
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