Graph Contrastive Learning via Noisy Training for Cold-Start Recommendation

This paper studies the problem of cold-start recommendation with graph contrastive learning. Graph contrastive learning has achieved state-of-the-art performance for the recommendation. However, it lacks robustness in cold-start scenarios due to noisy user–item interactions. Recent works have been proposed to improve the performance of noisy user-item interactions; however, they can achieve effective performance only on existing user–item interactions, which are not cold-start interactions. The question of how to find an optimal graph contrastive learning method that is suitable for cold-start cases still remains to be explored. We propose a novel method, graph contrastive learning via noisy training (GCLNT), to alleviate the cold-start recommendation problem. Specifically, GCLNT identifies user–item interactions with different preferences, and assigns them to different preference environments. With such different preference environments, noisy training is used to enhance the model’s robustness. We evaluate GCLNT on three datasets, and the results demonstrate the effectiveness of GCLNT in handling the cold-start in recommender systems.