Mutually reinforced network embedding: An integrated approach to research paper recommendation

作者：

Highlights：

• A novel network embedding model is proposed for scientific paper recommendation.

• Our model integrate co-authorship, text and network with mutual reinforcement.

• A inductive method is developed to recommend reference papers for cold-start papers.

• Our method achieves state-of-art performance in HepTh and AAN datasets.

摘要

•A novel network embedding model is proposed for scientific paper recommendation.•Our model integrate co-authorship, text and network with mutual reinforcement.•A inductive method is developed to recommend reference papers for cold-start papers.•Our method achieves state-of-art performance in HepTh and AAN datasets.

论文关键词：Research paper recommendation,Network embedding,Mutually reinforced model

论文评审过程：Received 24 January 2022, Revised 16 May 2022, Accepted 16 May 2022, Available online 24 May 2022, Version of Record 27 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117616