通过信息协同模块，让推荐系统更懂用户偏好。
PRISM: Personalized Recommendation via Information Synergy Module
- 拆解多模态信息为独特、重复和协同三类，精准建模
- 动态加权融合，提升推荐准确率，最高增益达12.3%
- 适配多种推荐模型，适合个性化推荐场景
多模态序列推荐（MSR）利用物品的多种模态信息提升推荐精度，但如何有效且自适应地融合仍具挑战。现有方法常忽略仅在模态组合中出现的协同信息，且默认不同模态交互对所有用户重要性固定。为此，本文提出可即插即用的PRISM框架，通过交互专家层将多模态信息分解为唯一、冗余与协同成分，并由用户偏好引导的自适应融合层动态加权。该信息论设计实现细粒度解耦与个性化融合。在四个数据集及三种序列推荐基线上的实验验证了其有效性与通用性。代码已开源。
原文摘要 · Abstract (English)
Multimodal sequential recommendation (MSR) leverages diverse item modalities to improve recommendation accuracy, while achieving effective and adaptive fusion remains challenging. Existing MSR models often overlook synergistic information that emerges only through modality combinations. Moreover, they typically assume a fixed importance for different modality interactions across users. To address these limitations, we propose \textbf{P}ersonalized \textbf{R}ecommend-ation via \textbf{I}nformation \textbf{S}ynergy \textbf{M}odule (PRISM), a plug-and-play framework for sequential recommendation (SR). PRISM explicitly decomposes multimodal information into unique, redundant, and synergistic components through an Interaction Expert Layer and dynamically weights them via an Adaptive Fusion Layer guided by user preferences. This information-theoretic design enables fine-grained disentanglement and personalized fusion of multimodal signals. Extensive experiments on four datasets and three SR backbones demonstrate its effectiveness and versatility. The code is available at https://github.com/YutongLi2024/PRISM.
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