User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation
Multi-modal recommendation (MMR) enriches item representations by introducing item content, e.g., visual and textual descriptions, to improve upon interaction-only recommenders. The success of MMR hinges on aligning these content modalities with user preferences derived from interaction data, yet dominant practices based on disentangling modality-invariant preference-driving signals from modality-specific preference-irrelevant noises are flawed. First, they assume a one-size-fits-all relevance of item content to user preferences for all users, which contradicts the user-conditional fact of preferences. Second, they optimize pairwise contrastive losses separately toward cross-modal alignme...
| Authors | Jing Du, Zesheng Ye, Congbo Ma et al. |
|---|---|
| Primary category | cs.IR |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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