MAGIC: Noise Mitigation and Knowledge Alignment for Knowledge Graph-Based Multi-modal Recommendation
Shijie Zhu, Yan Zhang, Li Zhang, Xi Chen, Lei Zhao · 2025
Multi-modal recommender systems (MMRSs) have demonstrated significant potential in mitigating data sparsity and cold start problems by leveraging diverse multi-modal data, such as text and images. To further improve the recommendation accuracy, some MMRSs have integrated knowledge graphs (KGs) to enrich the graph structure with meaningful relationships between entities, giving rise to the task of KG-based MMRSs. Despite the promising results achieved by existing studies on this task, (i) they overlook the substantial noise introduced within the auxiliary information (i.e., both KGs and multi-modal data), and (ii) most of them struggle to effectively align the knowledge from history user-item interactions and auxiliary information. To tackle these limitations, we propose a novel model entitled MAGIC (noise Mitigation and knowledge Aignment for knowledge Graph-based multI-modal reCommendation). Specifically, to tackle the limitation (i), we design a noise-aware heterogeneous aggregation layer in the KG-based modal enhancement module. To address the limitation (ii), MAGIC introduces adversarial learning in the CF-based adversarial learning module, and exploits contrastive learning in the fusion and prediction module. The experiments conducted on two extended real-world datasets from different domains demonstrate the superiority of MAGIC over state-of-the-art baselines.