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Existing research usually utilizes side information such as social network oritem attributes to improve the performance of collaborative filtering-basedrecommender systems. In this paper, the knowledge graph with user perception isused to acquire the source of side information. We proposed KGUPN to addressthe limitations of existing embedding-based and path-based knowledgegraph-aware recommendation methods, an end-to-end framework that integratesknowledge graph and user awareness into scientific and technological newsrecommendation systems. KGUPN contains three main layers, which are thepropagation representation layer, the contextual information layer andcollaborative relation layer. The propagation representation layer improves therepresentation of an entity by recursively propagating embeddings from itsneighbors (which can be users, news, or relationships) in the knowledge graph.The contextual information layer improves the representation of entities byencoding the behavioral information of entities appearing in the news. Thecollaborative relation layer complements the relationship between entities inthe news knowledge graph. Experimental results on real-world datasets show thatKGUPN significantly outperforms state-of-the-art baselines in scientific andtechnological news recommendation.

Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception