Social-aware graph contrastive learning for recommender systems
Yuanyuan Zhang, Junwu Zhu, Yonglong Zhang, Yi Qing Zhu, Jialuo Zhou, Yaling Xie · Applied Soft Computing · 2024
Recommender systems usually encounter the issue of sparse interaction data, which is commonly alleviated by social recommendation models based on graph neural networks . However, these models overlook the collaborative similarity relationship among items and fail to effectively integrate and process various graph structures. To address these issues, we propose a novel S ocial-aware G raph C ontrastive L earning R ecommendation model (SG-CLR). Specifically, we initially utilize data augmentation techniques to obtain different augmented views of user–item interaction. Secondly, a social-aware encoder is put forward to effectively capture both the influence diffusing within the social network and the attractiveness of items among the item collaborative similarity graph . Finally, we employ graph contrastive learning to maximize the consistency of node representation across different augmented views, and further focus on domain-shared information through joint training. Experimental results conducted on two real-world datasets demonstrate that the proposed SG-CLR outperforms the state-of-the-art baselines. Compared to the best baseline, SG-CLR improves the performance on the two datasets by 3.069% and 2.972%, respectively.