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.
Authors
- Mahdi Bashari (ORCID: https://orcid.org/0000-0002-9211-5475)
- Sara Salamat (ORCID: https://orcid.org/0009-0007-3676-6023)
- Negar Arabzadeh (ORCID: https://orcid.org/0000-0002-4411-7089)
- Ebrahim Bagheri (ORCID: https://orcid.org/0000-0002-5148-6237)
- Sajad Ebrahimi (ORCID: https://orcid.org/0009-0003-1630-3938)
Institutions
- Systems, Applications & Products in Data Processing (Canada) (CA)
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