This paper proposes Dual-Channel Graph Embedding for Multimodal Recommendation with a Causal Perspective (DCGCE), a representation learning framework for multimodal recommendation inspired by a causal perspective on user behavior that consistently outperforms state-of-the-art baselines across multiple evaluation metrics, while providing improved interpretability in user preference modeling.
Experiments show that DAURA consistently outperforms all baselines across product and restaurant recommendation domains, while ablation studies validate the contribution of each module.
Recommendation systems play a crucial role in efficiently filtering vast amounts of information, and supporting businesses across various domains such as e-commerce, social media, and online entertainment. Traditional recommendation methods, including collaborative filtering, often suffer from data sparsity issues, lim...
The proposed LLM-augmented Multi-Graph Contrastive Learning (LLM-MGCL) is a multi-graph neural network that uses semantic and spatial information about items to extend the LightGCN backbone with two auxiliary item-item graphs that outperforms classical collaborative filtering, matrix factorization, and interaction-only...
User rating prediction is a fundamental task in recommendation systems, whose performance critically depends on accurate modeling of fine-grained user preferences. Existing rating-aware methods, however, face two main limitations: they fail to adequately capture the interaction-dependent relationship between user ratin...
Hui-Yi Zhang, Shuai-Yang Li, Sheng Sang et al.· Intelligent Data Analysis· 0 citations
Cusality-enhanced Graph Contrastive Learning for Explainable Recommendation (CGCLER) is proposed, which enables item–explanation joint ranking by distinguishing causal and confounding features at the graph-node representation level and introduces a backdoor-inspired graph contrastive learning objective.
