Abstract
Recommendation System is one of the effective tools to solve the problem of information overload in the era of big data, but the data sparsity has greatly affected its performance. Recently, contrastive learning, has attracted great attention and is expected to solve this problem. However, most of the existing graph-based contrastive learning methods perturb the original graph for data enhancement, which may affect the recommended performance. Meanwhile, studies have shown that improving the uniformity of data distribution is more important than data augmentation by graph perturbation. In this paper, in order to improve the uniformity of the data distribution, we propose a Graph Contrastive Learning with Hybrid Noise Augmentation for Recommendation, which is abbreviated as GCLHANRec. Specifically, we add uniform distribution random noise to users and normal distribution random noise to items, to improve the data uniformity while increasing the user’s interest diversity for different items, thereby improving the accuracy and personalization degree of the recommendation system. Additionally, we propose Balanced Bayesian Personalized Ranking (BBPR) as the loss function for recommendation tasks, which is a modification of BPR to better make the model pay more attention to the difference between positive and negative samples, thus performing better in ranking tasks. We conducted extensive experiments using three datasets collected from actual environment, including Movielens, LastFM and Douban-book. The results show that our method outperforms several existing methods.
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Acknowledgments
The work reported herein was supported by National Key R &D Program (2020YFB1406900), National Natural Science Foundation of China (62172324, 62102310), Key R &D in Shaanxi Province (2023-YBGY-269, 2022QCY-LL-33HZ).
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Zhu, K., Qin, T., Wang, X., Chen, Z., Ding, J. (2023). Graph Contrastive Learning with Hybrid Noise Augmentation for Recommendation. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_23
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DOI: https://doi.org/10.1007/978-3-031-46674-8_23
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