Joint Representation of Entities and Relations via Graph Attention Networks for Explainable Recommendations

The latest advances in Graph Neural Networks (GNNs), have provided important new ideas for solving the Knowledge Graph (KG) representation problem for recommendation purposes. Although GNNs have an effective graph representation capability, the nonlinear transformations over the layers cause a loss of semantic information and make the generated embeddings hard to explain. In this paper, we investigate the potential of large KGs to perform interpretable recommendation using Graph Attention Networks (GATs). Our goal is to fully exploit the semantic information and preserve inherent knowledge ported in relations by jointly learning low-dimensional embeddings for nodes (i.e., entities) and edges (i.e., properties). Specifically, we feed the original data with additional knowledge from the Linked Open Data (LOD) cloud, and apply GATs to generate a vector representation for each node on the graph. Experiments conducted on three real-world datasets for the top-K recommendation task demonstrate the state-of-the-art performance of the system proposed. In addition to improving predictive performance in terms of precision, recall, and diversity, our approach fully exploits the rich structured information provided by KGs to offer explanation for recommendations.