Work

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

Year: 2026

Type: article

Abstract: Multimodal Recommender Systems (MRSs) leverage heterogeneous data to alleviate datasparsity, yet they remain vulnerable to spurious correlations and modality noise that obscurethe rationale behind recommendations. To address this, we propose the MultimodalCounterfactual Fusion with the Causality-Guided Contrastive Learning (MCFC) framework.MCFC integrates three core modules: a Multimodal Structural Causal Model thatmotivates the assumed causal paths of multimodal biases; a Counterfactual Feature Fusionmodule with causal effect-inspired weighting that estimates each modality’s contributionvia score perturbations and suppresses noisy features; and a Causality-Guided ContrastiveLearning mechanism that builds counterfactual in-batch negatives to enforce invariantand robust representations. Extensive experiments on four public Amazon datasets showthat 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 robustnessto modality noise and strong adaptability in sparse interaction settings.

Source: Future Internet

DOI: 10.3390/fi18100551

Authors: Shuyun Fang, Junling Wang, Xinxin Shao, Derui Qi, Fukun Chen

Institutions: Dalian Neusoft University of Information, Liaoning Normal University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: Department of Education of Liaoning Province