A deep learning knowledge graph neural network for recommender systems

Knowledge graphs are becoming the new state-of-the-art for recommender systems. This paper is based on knowledge graphs to alleviate the problem of data sparsity. Various methods have been recently deployed to solve this problem which largely attempts to study user-item representation and then recommend items to users based on these representations. Although these methods are effective, they lack explainability for recommendations and do not mine side information. In this paper, we propose the use of knowledge graphs which includes additional information about users and items in addition to the use of a user/item interaction matrix. The vital element of our model is neighbourhood aggregation for collaborative filtering. Every user and item are associated with an ID embedding, which is circulated on the interaction graph for users, items, and their attributes. We obtain the final embeddings by combining the embeddings learned at various hidden layers with a biased sum. Our model is easier to train and achieves better performance compared to graph neural network-based collaborative filtering (GCF) and other state-of-the-art recommender methods. We provide evidence for our argument by analytically comparing the knowledge graph convolution network (KGCN) with GCF and eight other state-ofthe-art methods, using similar experimental settings and the same datasets.