TY - JOUR AU - Cheng, Zhangtao AU - Zhong, Ting AU - Zhang, Kunpeng AU - Walker, Joojo AU - Zhou, Fan PY - 2022/06/28 Y2 - 2024/03/29 TI - Learning Contrastive Multi-View Graphs for Recommendation (Student Abstract) JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 11 SE - AAAI Student Abstract and Poster Program DO - 10.1609/aaai.v36i11.21600 UR - https://ojs.aaai.org/index.php/AAAI/article/view/21600 SP - 12927-12928 AB - 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 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. ER -