One-Class Recommendation through Unsupervised Graph Neural Networks for Link Prediction - Gôlo, Marcos P. S.; Moraes, Leonardo G.; Goularte, Rudinei; Marcacini, Ricardo M. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2022: Anais do X Symposium on Knowledge Discovery, Mining and Learning, 2022)
Recommender systems play a key role in every online platform to provide users a better experience. Many classic recommendation approaches might find issues, mainly modeling user relations. Graphs can naturally model these relations since we can connect users interacting with items. On the other hand, when we model user-item relations through graphs, we do not have interactions between all users and items. In addition, there are few non-recommendation interactions, which makes it challenging to cover this scope. Also, the scope of what will not be recommended for the user is greater than what will be recommended. An alternative is One-Class Learning (OCL) which is able to recommend or not an item for a user only to train with recommendations, mitigating the needing to cover the scope of non-recommendations. However, OCL and Recommender Systems need appropriate, adequate, and robust representations to perform the recommendations in the best possible way. Therefore, we propose the one-class recommendation via representations learned by unsupervised graph neural networks (GNNs) for link prediction to generate a more robust and meaningful representation of users and items. In the results, our GNNs for link prediction outperform other methods to represent the users and items in the one-class recommendation. Furthermore, our proposal also outperforms a GNN for link prediction. Thus, our proposal recommended better and learned more robust representations.