DivGCL: A Graph Contrastive Learning Model for Diverse Recommendation

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.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i16.33852
Citations
1
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
1.96

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article

DivGCL: A Graph Contrastive Learning Model for Diverse Recommendation

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1 citations
Proceedings of the AAAI Conference on Artificial Intelligence
Recommender Systems and Techniques
1.96
article

DivGCL: A Graph Contrastive Learning Model for Diverse Recommendation

Haolong Xiang, Xiaolong Xu, Lianyong Qi, Yangli‐ao Geng, Yifan Zhu, Dan Zhang, Amin Beheshti, Xuyun Zhang, Wenwen Gong
article en
1 citations

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.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(16)
Beijing University of Posts and Telecommunications (CN), Nanjing University of Information Science and Technology (CN), China University of Petroleum, Beijing (CN), Beijing Jiaotong University (CN), University of Petroleum (ID), China Agricultural University (CN), Macquarie University (AU), Tsinghua University (CN)
Australian Government, National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Shandong Province
Openalex Percentile: Top 15%
Recommender Systems and Techniques
1.96
DOIOpen Access PDFOpenAlex
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