A Graph-Based Recommendation Model Using Contrastive Learning for Inductive Scenario

Graph-based recommendation models, which utilize a user-item interaction graph whose edges represent users' preferences, are well known because of their effectiveness. However, many existing models are intrinsically transductive, meaning they can make recommendations only for users and items that exist in the training data. To solve this challenge, some researchers focus on recommendations in the inductive scenario where new users and new items emerge in the inference phase. In this paper, we explore the potential of contrastive learning for the inductive scenario to further improve recommendation accuracy. The situation where new users and new items emerge can be interpreted as a change in the interaction graph. Therefore, it is crucial to capture the change in the interaction graph for inductive recommendations. Based on the above perspective, we propose the contrastive learning framework, INductive Contrastive Learning (INCL). INCL creates an augmented graph by adding or removing edges of the original interaction graph and simulates changes in the interaction graph. By performing contrastive learning, INCL fosters each representation generated from the original interaction graph and the augmented graph to be similar. As a result, INCL is trained to generate robust representations that can adapt to changes in the interaction graph. Experimental results on real-world datasets demonstrate that INCL outperforms existing models in the inductive scenario, achieving up to 2.13% improvement in Recall@20.