Context-aware explainable recommendations over knowledge graphs

TL;DR

This paper introduces CA-KGCN, a framework that models user preferences in recommender systems by incorporating knowledge graphs and user context, improving prediction accuracy and providing context-aware explanations.

Contribution

The paper presents a novel end-to-end framework that integrates context-awareness into knowledge graph-based recommendations, enhancing both accuracy and explainability.

Findings

Effective modeling of user preferences with context adaptation

Improved recommendation accuracy demonstrated on real-world datasets

Enhanced explainability tailored to user contexts

Abstract

Knowledge graphs contain rich semantic relationships related to items and incorporating such semantic relationships into recommender systems helps to explore the latent connections of items, thus improving the accuracy of prediction and enhancing the explainability of recommendations. However, such explainability is not adapted to users' contexts, which can significantly influence their preferences. In this work, we propose CA-KGCN (Context-Aware Knowledge Graph Convolutional Network), an end-to-end framework that can model users' preferences adapted to their contexts and can incorporate rich semantic relationships in the knowledge graph related to items. This framework captures users' attention to different factors: contexts and features of items. More specifically, the framework can model users' preferences adapted to their contexts and provide explanations adapted to the given context. Experiments on three real-world datasets show the effectiveness of our framework: modeling users' preferences adapted to their contexts and explaining the recommendations generated.