AAAI Conference 2022 Short Paper
Learning Contrastive Multi-View Graphs for Recommendation (Student Abstract)
- Zhangtao Cheng
- Ting Zhong
- Kunpeng Zhang
- Joojo Walker
- Fan Zhou
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.