Toward Paper Recommendation by Jointly Exploiting Diversity and Dynamics in Heterogeneous Information Networks

Paper Recommendation Using SPECTER with Low-Rank and Sparse Matrix Factorization

“…Recently, complex graph and network representation techniques have used semantic relationships between graphs or network nodes to learn vector representations of corresponding nodes. Various paper recommendation models [37,38] use this embedding method to propose recommendations. For this reason, Gupta and Varma [39] used Doc2vec [40] and DeepWalk [41], respectively, to learn the embedding of paper content and network structure.…”