Context-aware explainable recommendations over knowledge graphs
This addresses the need for personalized and interpretable recommendations in systems like e-commerce or streaming services, but it is incremental as it builds on existing knowledge graph methods.
The paper tackles the problem of context-aware explainable recommendations by proposing CA-KGCN, which models user preferences and provides explanations adapted to user contexts, showing effectiveness on three real-world datasets.
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.