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

With the sharp increase in the volume of literature data, researchers must spend considerable time and energy locating desired papers.A paper recommendation is the means necessary to solve this problem.Unfortunately, the large amount of data combined with sparsity makes personalizing papers challenging.Traditional matrix decomposition models have cold-start issues.Most overlook the importance of information and fail to consider the introduction of noise when using side information, resulting in unsatisfactory recommendations.This study proposes a paper recommendation method (PR-SLSMF) using document-level representation learning with citation-informed transformers (SPECTER) and low-rank and sparse matrix factorization; it uses SPECTER to learn paper content representation.The model calculates the similarity between papers and constructs a weighted heterogeneous information network (HIN), including citation and content similarity information.This method combines the LSMF method with HIN, effectively alleviating data sparsity and cold-start issues and avoiding topic drift.We validated the effectiveness of this method on two real datasets and the necessity of adding side information.