Causal Inspired Multi Modal Recommendation

• URL: http://arxiv.org/abs/2510.12325v1
• Date: Tue, 14 Oct 2025 09:29:07 GMT
• Title: Causal Inspired Multi Modal Recommendation
• Authors: Jie Yang, Chenyang Gu, Zixuan Liu,
• Abstract summary: Multimodal recommender systems enhance personalized recommendations in e-commerce and online advertising.<n>Existing methods often overlook two critical biases: modal confounding and interaction bias.<n>We propose a Causal-inspired multimodal Recommendation framework to address these challenges.
• Score: 7.356256402158066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal recommender systems enhance personalized recommendations in e-commerce and online advertising by integrating visual, textual, and user-item interaction data. However, existing methods often overlook two critical biases: (i) modal confounding, where latent factors (e.g., brand style or product category) simultaneously drive multiple modalities and influence user preference, leading to spurious feature-preference associations; (ii) interaction bias, where genuine user preferences are mixed with noise from exposure effects and accidental clicks. To address these challenges, we propose a Causal-inspired multimodal Recommendation framework. Specifically, we introduce a dual-channel cross-modal diffusion module to identify hidden modal confounders, utilize back-door adjustment with hierarchical matching and vector-quantized codebooks to block confounding paths, and apply front-door adjustment combined with causal topology reconstruction to build a deconfounded causal subgraph. Extensive experiments on three real-world e-commerce datasets demonstrate that our method significantly outperforms state-of-the-art baselines while maintaining strong interpretability.

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