PRISM: Personalized Recommendation via Information Synergy Module

TL;DR

PRISM is a novel framework for multimodal sequential recommendation that explicitly models and personalizes the fusion of unique, redundant, and synergistic information from multiple modalities, improving recommendation accuracy.

Contribution

It introduces an information-theoretic approach with an Interaction Expert Layer and Adaptive Fusion Layer for personalized multimodal information fusion in sequential recommendation.

Findings

Outperforms existing models on four datasets

Effectively disentangles multimodal information components

Demonstrates versatility across different SR backbones

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.