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Prototype learning based hierarchical decoupling for multimodal recommendation
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DOI：10.1016/j.eswa.2025.130763.png)
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Abstract
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Multimodal recommendation systems, which utilize heterogeneous information such as text and images to enhance user interest modeling, have become core components of online services. A common paradigm involves extracting multimodal features via encoders and integrating them into collaborative filtering (CF) frameworks. However, this integration process suffers from multi-level coupling: (1) Cross-path Coupling (where user ID embeddings are forcibly shared between the collaborative and modal paths, introducing noise propagation and distorting preference learning); (2) Inter-Modal Redundant Coupling (where extensive redundant information across modalities obscures discriminative knowledge, resulting in the fragmentation of critical information specific to modality).
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