Reviewerly: Modeling the Reviewer Assignment Task as an Information Retrieval Problem

The peer review process is a fundamental aspect of academic publishing, ensuring the quality and credibility of scholarly work. In this talk, we will explore the critical challenges associated specifically with the assignment of reviewers to submitted papers. We will introduce Reviewerly, our innovative solution designed to enhance the efficiency and effectiveness of reviewer assignments by leveraging data from diverse sources, including OpenAlex, PubMed, and DBLP. By modeling the reviewer assignment problem as an information retrieval task, we focus on retrieving a pool of relevant and diverse reviewers for each paper.

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Publication Details

Published
2024-10-20
DOI
https://doi.org/10.1145/3627673.3679081
Citations
7
Primary Topic
Expert finding and Q&A systems
Type
article
Field-Weighted Citation Impact
5.45
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article

Reviewerly: Modeling the Reviewer Assignment Task as an Information Retrieval Problem

Mahdi Bashari, Sara Salamat, Negar Arabzadeh, Ebrahim Bagheri et al.
7 citations
Expert finding and Q&A systems
5.45
article

Reviewerly: Modeling the Reviewer Assignment Task as an Information Retrieval Problem

Mahdi Bashari, Sara Salamat, Negar Arabzadeh, Ebrahim Bagheri, Sajad Ebrahimi
article en
7 citations

Abstract

The peer review process is a fundamental aspect of academic publishing, ensuring the quality and credibility of scholarly work. In this talk, we will explore the critical challenges associated specifically with the assignment of reviewers to submitted papers. We will introduce Reviewerly, our innovative solution designed to enhance the efficiency and effectiveness of reviewer assignments by leveraging data from diverse sources, including OpenAlex, PubMed, and DBLP. By modeling the reviewer assignment problem as an information retrieval task, we focus on retrieving a pool of relevant and diverse reviewers for each paper.

Systems, Applications & Products in Data Processing (Canada) (CA)
Openalex Percentile: Top 4%
Expert finding and Q&A systems
5.45
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