Learning Contrastive Multi-View Graphs for Recommendation (Student Abstract)
Keywords:Graph Neural Networks, Contrastive Learning, Recommender System, Data Augmentation
AbstractThis 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 post-training 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.
How to Cite
Cheng, Z., Zhong, T., Zhang, K., Walker, J., & Zhou, F. (2022). Learning Contrastive Multi-View Graphs for Recommendation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12927-12928. https://doi.org/10.1609/aaai.v36i11.21600
AAAI Student Abstract and Poster Program