A proactive decision support system for reviewer recommendation in academia

作者：

Highlights：

• Relevance, authority, expertise, diversity, and conflict of interest are considered.

• Experiments on NIPS and AMiner dataset show effectiveness of proposed system.

• The temporal changes of reviewers’ interest are incorporated.

• Evaluated in terms of precision, nDCG, MRR, authority and expertise.

• Investigated tradeoff between diversity, and coverage vs. precision.

摘要

•Relevance, authority, expertise, diversity, and conflict of interest are considered.•Experiments on NIPS and AMiner dataset show effectiveness of proposed system.•The temporal changes of reviewers’ interest are incorporated.•Evaluated in terms of precision, nDCG, MRR, authority and expertise.•Investigated tradeoff between diversity, and coverage vs. precision.

论文关键词：Reviewer recommendation,Topic modeling,Clustering,Citation analysis,Random walk with restart (RWR)

论文评审过程：Received 16 September 2020, Revised 14 November 2020, Accepted 14 November 2020, Available online 19 November 2020, Version of Record 10 February 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114331