SKGCR: self-supervision enhanced knowledge-aware graph collaborative recommendation

A knowledge graph (KG) can be used as supplementary information for improving the performance of recommendations and it plays an increasingly important role in the recommendation area. As graph neural networks (GNNs) have advanced, propagation-based models that propagate user-item interactive data in the KG as a means of enhancing the representations of users and items have achieved state-of-the-art (SOTA) performance. The existing propagation-based models adopt the only supervised learning paradigm, which is reliant on the label information of the data. In addition, the features of the data itself, which are also known as self-supervised signals, can be employed for supporting supervised learning in improving model performance. This paper proposes the introduction of self-supervised learning (SSL) as a means of exploring the self-supervised signals in the data and a new recommendation model, Self-Supervision Enhanced Knowledge-aware Graph Collaborative Recommendation (SKGCR) is proposed. A unique feature of SKGCR is that it models each node in the user-item interaction data from multiple views for exploring the self-supervised signals. Multiple views are constructed through the random deletion of a certain percentage of interactions from the user-item interaction data. In addition, adopting the information noise contrastive estimation (InfoNCE) is proposed for maximizing the mutual information of the same node in different views. The results from this step serve as the self-supervised signals for training SKGCR, together with the supervised learning signals in the form of multi-task training. The experimental results demonstrate that the proposed SKGCR outperforms SOTA baselines.