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

• Panpan Guo,
• Gang Zhou,
• Jicang Lu,
• Zhufeng Li,
• Taojie Zhu,

Vol. 18, No. 5, May 31, 2024
10.3837/tiis.2024.05.002, Download Paper (Free):

• paper recommendation
• SPECTER
• low-rank and sparse matrix factorization
• heterogeneous information network

Abstract

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.

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