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arXiv · 2601.10944

PRISM: Personalized Recommendation via Information Synergy Module

Xinyi Zhang·Yutong Li·Peijie Sun·Letian Sha·Zhongxuan Han
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Abstract

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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Xinyi Zhang, Yutong Li, Peijie Sun, Letian Sha, Zhongxuan Han. 2026-01-16. PRISM: Personalized Recommendation via Information Synergy Module. https://arxiv.org/abs/2601.10944

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