Learning Contrastive Multi-View Graphs for Recommendation (Student Abstract)

This paper exploits self-supervised learning (SSL) to learn more accurate and robust representations from the user-item interaction graph. Particularly, we propose a novel SSL model that effectively leverages contrastive multi-view learning and pseudo-siamese network to construct a pre-training and posttraining framework. Moreover, we present three graph augmentation techniques during the pre-training stage and explore the effects of combining different augmentations, which allow us to learn general and robust representations for the GNN-based recommendation. Simple experimental evaluations on real-world datasets show that the proposed solution significantly improves the recommendation accuracy, especially for sparse data, and is also noise resistant.

Authors

• Zhangtao Cheng University of Electronic Science and Technology of China
• Ting Zhong University of Electronic Science and Technology of China
• Kunpeng Zhang University of Maryland, College Park
• Joojo Walker
• Fan Zhou University of Electronic Science and Technology of China

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