PSRMTE: Paper submission recommendation using mixtures of transformer

作者：

Highlights：

• Bidirectional transformer encoders can improve the performance of the paper submission recommendation system.

• The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem.

• Proposed techniques can surpass other recent techniques on two datasets related.

摘要

•Bidirectional transformer encoders can improve the performance of the paper submission recommendation system.•The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem.•Proposed techniques can surpass other recent techniques on two datasets related.

论文关键词：Recommendation system,Deep learning,Transformer encoders

论文评审过程：Received 6 June 2021, Revised 15 December 2021, Accepted 28 March 2022, Available online 20 April 2022, Version of Record 5 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117096