RE-KGR: Relation-Enhanced Knowledge Graph Reasoning for Recommendation

A knowledge graph (KG) has been widely adopted to improve recommendation performance. The multi-hop user-item connections in a KG can provide reasons for recommending an item to a user. However, existing methods do not effectively leverage the relations of entities and interpretable paths in a KG. To address this limitation, in this paper, we propose a novel recommendation framework called relation-enhanced knowledge graph reasoning for recommendation (RE-KGR) that combines recommendation and explainability by reasoning user-item interaction paths (UIIPs). First, instead of applying an alignment algorithm for preprocessing, RE-KGR directly learns the semantic representation of entities from structured knowledge by stacking relation-based convolutional layers to take full advantage of the KG. Moreover, RE-KGR infers user preferences by calculating the sum of all UIIPs between users and items. Finally, RE-KGR selects several UIIPs with the highest probabilities as possible reasons for the recommendations. Extensive experiments on three real-world datasets demonstrate that our proposed method significantly outperforms several state-of-the-art baselines and achieves superior performance and explainability.