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A Situation-aware Enhancer for Personalized Recommendation

Jiayu Li, Peijie Sun, Chumeng Jiang, Weizhi Ma, Qingyao Ai, Min Zhang

TL;DR

This work reframes situations as preconditions for user-item interactions rather than ordinary features, enabling a dedicated, pluggable enhancer (SARE) that decouples situations from user/item representations. SARE comprises a Personalized Situation Fusion (PSF) and a User-Conditioned Preference Encoder (UCPE) to model how situations are perceived and how they alter preferences, with a probabilistic combiner that fuses backbone and SARE signals based on confidence. Empirical evaluation across seven backbones and two real-world datasets demonstrates significant, consistent gains over context-aware and time-aware baselines, and confirms SARE’s effectiveness in both context-aware and ID-based settings. The approach offers a flexible, low-parameter-addition path to richer, situation-aware personalized recommendations with practical implications for deploying adaptive recommender systems in dynamic environments.

Abstract

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.

A Situation-aware Enhancer for Personalized Recommendation

TL;DR

This work reframes situations as preconditions for user-item interactions rather than ordinary features, enabling a dedicated, pluggable enhancer (SARE) that decouples situations from user/item representations. SARE comprises a Personalized Situation Fusion (PSF) and a User-Conditioned Preference Encoder (UCPE) to model how situations are perceived and how they alter preferences, with a probabilistic combiner that fuses backbone and SARE signals based on confidence. Empirical evaluation across seven backbones and two real-world datasets demonstrates significant, consistent gains over context-aware and time-aware baselines, and confirms SARE’s effectiveness in both context-aware and ID-based settings. The approach offers a flexible, low-parameter-addition path to richer, situation-aware personalized recommendations with practical implications for deploying adaptive recommender systems in dynamic environments.

Abstract

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.
Paper Structure (24 sections, 8 equations, 4 figures, 3 tables)

This paper contains 24 sections, 8 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Context-aware Recommendation
  4. Situation-aware Recommendation
  5. Task Definition
  6. The Situation-Aware Recommender Enhancer
  7. Overview of the Framework
  8. Structure of SARE
  9. User-Conditioned Preference Encoder (UCPE)
  10. Personalized Situation Fusion (PSF)
  11. Probability Combiner and Model Learning
  12. Discussions on Parameter Efficiency
  13. Experiments
  14. Experimental Settings
  15. Datasets
  16. ...and 9 more sections

Figures (4)

  • Figure 1: An example of situations as background for interactions in a news RecSys, where perceptions of situations and situations' influence on users' preference are personalized.
  • Figure 2: Overview of our proposed Situation-Oriented Optimizer (SARE). (a) SARE can enhance backbone recommender systems with various (optional) types of input. (b) The detailed structure of SARE, where user history is an optional input. (c) The details of user-conditioned preference encoder, one of the two key components of SARE.
  • Figure 3: Comparisons between xDeepFM+SARE and SOTA situation (time)-aware models. xDeepFM+SARE outperforms all baselines significantly.
  • Figure 4: Ablation study with SARE and its variant, without: combiner(-cb., using SARE output only), ucfe, psf, confidence-aware weights(-conf., harmonic mean without weights). The dotted lines denote backbone performances.