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Context-aware explainable recommendations over knowledge graphs

arXiv 2310.16141 ↗First posted 24 Oct 2023cs.IR cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 24 Oct 2023.

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11 more
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