Jun-Ling Wang

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

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 L...