Through multi-layer propagation, the structured information and historical preference information are embedded into a unified vector space, and the potential user–item vector representation is expanded. The fusion of structural and contextual information is used to construct fine-grained knowledge graphs via knowledge graph embedding methods and to generate initial embedding representations. This framework uses a knowledge graph as a source of auxiliary information to extract user–item interaction information and build a sub-knowledge base. To verify that knowledge graphs can further alleviate the above problems, this paper proposes an end-to-end framework that uses attentive knowledge graph perceptual propagation for recommendations (AKGP). Knowledge graphs (KGs) are widely used to improve recommendation performance.
Collaborative filtering (CF) usually suffers from data sparsity and cold starts.
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