Context-aware explainable recommendations over knowledge graphs
- URL: http://arxiv.org/abs/2310.16141v1
- Date: Tue, 24 Oct 2023 19:30:31 GMT
- Title: Context-aware explainable recommendations over knowledge graphs
- Authors: Jinfeng Zhong, Elsa Negre
- Abstract summary: We propose an end-to-end framework that can model users' preferences adapted to their contexts.
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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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