KGTORe: Tailored Recommendations through Knowledge-aware GNN Models

Knowledge graphs (KG) have been proven to be a powerful source of side information to enhance the performance of recommendation algorithms. Their graph-based structure paves the way for the adoption of graph-aware learning models such as Graph Neural Networks (GNNs). In this respect, state-of-the-art models achieve good performance and interpretability via user-level combinations of intents leading users to their choices. Unfortunately, such results often come from and end-to-end learnings that considers a combination of the whole set of features contained in the KG without any analysis of the user decisions. In this paper, we introduce KGTORe, a GNN-based model that exploits KG to learn latent representations for the semantic features, and consequently, interpret the user decisions as a personal distillation of the item feature representations. Differently from previous models, KGTORe does not need to process the whole KG at training time but relies on a selection of the most discriminative features for the users, thus resulting in improved performance and personalization. Experimental results on three well-known datasets show that KGTORe achieves remarkable accuracy performance and several ablation studies demonstrate the effectiveness of its components. The implementation of KGTORe is available at: https://github.com/sisinflab/KGTORe.