KPAR: Knowledge-aware Path-based Attentive Recommender with Interpretability

Abstract

Knowledge Graph (KG)-based recommender systems utilize both Collaborative Filtering (CF) data and informative KG data to improve prediction accuracy. Path-based KG methods are a family of KG-based recommendation models that explore the interlinks within a knowledge graph in order to enhance the connectivity between users and items with rich complementary information. A key advantage of path-based KG methods stems from their ability to enable intuitive explanations naturally. In this work, we present a novel path-based algorithm that employs neural attention in order to better extract the relevant information from the unified graph. Evaluations based on public KG recommendation datasets indicate a clear advantage to the proposed method compared to state-of-the-art path-based alternatives. Furthermore, we show that this advantage also extends to cold items where a better utilization of the KG leads to improved predictions in cases where no CF data is available. Finally, by performing attention-score analysis, we demonstrate the ability of our approach to provide better interpretability into the model’s inner workings as well as extract more intuitive explanations. The code for this work is publicly available on GitHub: https://github.com/DeltaLabTLV/KPAR.