DivGCL: A Graph Contrastive Learning Model for Diverse Recommendation
Wenwen Gong, Yangliao Geng, Dan Zhang, Yifan Zhu, Xiaolong Xu, Haolong Xiang, Amin Beheshti, Xuyun Zhang
Abstract
Graph Contrastive Learning (GCL), as a primary paradigm of graph self-supervised learning, spurs a fruitful line of research in tackling the data sparsity issue by maximizing the consistency of user/item embeddings between different augmented views with random perturbations. However, diversity, as a crucial metric for recommendation performance and user satisfaction, has received rather little attention. In fact, there exists a challenging dilemma in balancing accuracy and diversity. To address these issues, we propose a new Graph Contrastive Learning (DivGCL) model for diversifying recommendations. Inspired by the excellence of the determinant point process (DPP), DivGCL adopts a DPP likelihood-based loss function to achieve an ideal trade-off between diversity and accuracy, optimizing it jointly with the advanced Gaussian noise-augmented GCL objective. Extensive experiments on four popular datasets demonstrate that DivGCL surpasses existing approaches in balancing accuracy and diversity, with an improvement of 23.47% at T@20 (abbreviation for trade-off metric) on ML-1M.
BibTeX
@article{Gong_Geng_Zhang_Zhu_Xu_Xiang_Beheshti_Zhang_Qi_2025, title={DivGCL: A Graph Contrastive Learning Model for Diverse Recommendation}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33852}, DOI={10.1609/aaai.v39i16.33852}, abstractNote={Graph Contrastive Learning (GCL), as a primary paradigm of graph self-supervised learning, spurs a fruitful line of research in tackling the data sparsity issue by maximizing the consistency of user/item embeddings between different augmented views with random perturbations. However, diversity, as a crucial metric for recommendation performance and user satisfaction, has received rather little attention. In fact, there exists a challenging dilemma in balancing accuracy and diversity. To address these issues, we propose a new Graph Contrastive Learning (DivGCL) model for diversifying recommendations. Inspired by the excellence of the determinant point process (DPP), DivGCL adopts a DPP likelihood-based loss function to achieve an ideal trade-off between diversity and accuracy, optimizing it jointly with the advanced Gaussian noise-augmented GCL objective. Extensive experiments on four popular datasets demonstrate that DivGCL surpasses existing approaches in balancing accuracy and diversity, with an improvement of 23.47% at T@20 (abbreviation for trade-off metric) on ML-1M.}, number={16}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Gong, Wenwen and Geng, Yangliao and Zhang, Dan and Zhu, Yifan and Xu, Xiaolong and Xiang, Haolong and Beheshti, Amin and Zhang, Xuyun and Qi, Lianyong}, year={2025}, month={Apr.}, pages={16853-16861} }