Multimodal Counterfactual Fusion with Causality-Guided Contrastive Learning for Noise-Robust Recommendation

Abstract

Multimodal Recommender Systems (MRSs) leverage heterogeneous data to alleviate data sparsity, yet they remain vulnerable to spurious correlations and modality noise that obscure the rationale behind recommendations. To address this, we propose the Multimodal Counterfactual Fusion with the Causality-Guided Contrastive Learning (MCFC) framework. MCFC integrates three core modules: a Multimodal Structural Causal Model that motivates the assumed causal paths of multimodal biases; a Counterfactual Feature Fusion module with causal effect-inspired weighting that estimates each modality’s contribution via score perturbations and suppresses noisy features; and a Causality-Guided Contrastive Learning mechanism that builds counterfactual in-batch negatives to enforce invariant and robust representations. Extensive experiments on four public Amazon datasets show that MCFC consistently outperforms state-of-the-art baselines on Recall@K and NDCG@K, improving Recall@20 by 29.9% over the strongest baseline on the highly sparse Baby dataset. Ablation studies confirm the contribution of each module, evidencing MCFC’s robustness to modality noise and strong adaptability in sparse interaction settings.