Dual Interest Learning with Context-Aware Adaptive Interaction for Social Recommendation

Social recommendation utilizes social relations to extract auxiliary collaborative signals, effectively mitigating data sparsity issues. However, existing approaches predominantly focus on static influence from social friends while neglecting two critical aspects: the dynamic contextual patterns in user behaviors and the potential of collaborative users. To address these limitations and further alleviate data sparsity, we propose a context-aware dual graph attention network (CDGA) that simultaneously captures users’ static and dynamic interests through social relations and interaction records. The proposed CDGA model introduces a dynamic activation mechanism to simulate contextual influences, generating dynamic embeddings for users and items. Furthermore, we develop an adaptive fusion mechanism that integrates interaction channels across static and dynamic embeddings for interaction prediction. Extensive experiments on three benchmark datasets demonstrate that CDGA consistently outperforms state-of-the-art social recommendation methods, confirming its effectiveness.