Content-based and knowledge graph-based paper recommendation: Exploring user preferences with the knowledge graphs for scientific paper recommendation

Researchers usually face difficulties in finding scientific papers relevant to their research interests due to increasing growth. Recommender systems emerge as a leading solution to filter valuable items intelligently. Recently, deep learning algorithms, such as convolutional neural network, improved traditional recommendation technologies, for example, the graph-based or content-based methods. However, existing graph-based methods ignore high-order association between users and items on graphs, and content-based methods ignore global features of texts for explicit user preferences. Therefore, this paper proposes a Content-based and knowledge Graph-based Paper Recommendation method (CGPRec), which uses a two-layer self-attention block to obtain global features of texts for more complete explicit user preferences, and proposes an improved graph convolutional network for modeling high-order associations on the knowledge graph to mine implicit user preferences. And the knowledge graph in this paper is constructed with concept nodes, user nodes, paper nodes, and other meta-data nodes. Experimental results on a public dataset, CiteULike-a, and a real application log dataset, AHData, show that our model outperforms compared with baseline methods.