Global citation recommendation employing generative adversarial network

作者：

Highlights：

• We construct a Heterogeneous Bibliographic Network, which exploits semantics among the network objects.

• We propose a GAN-based network embedding model to address the network sparsity problem.

• We propose a citation recommendation model to produce personalized results corresponding to researchers' preferences.

• We conduct extensive experiments on two real-world datasets and prove the significance of the proposed model.

摘要

•We construct a Heterogeneous Bibliographic Network, which exploits semantics among the network objects.•We propose a GAN-based network embedding model to address the network sparsity problem.•We propose a citation recommendation model to produce personalized results corresponding to researchers' preferences.•We conduct extensive experiments on two real-world datasets and prove the significance of the proposed model.

论文关键词：Recommender systems,Citation recommendation,Network embedding,Generative adversarial network,Deep learning,Sparsity

论文评审过程：Received 19 July 2020, Revised 25 February 2021, Accepted 6 March 2021, Available online 16 March 2021, Version of Record 8 May 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114888