# Reviewer assignment problem: A scoping review

arXiv: https://arxiv.org/abs/2305.07887 · HTML: https://mlsanity.com/paper/2305.07887/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 61 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 8 of 8)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [A Survey on Contextual Embeddings](https://mlsanity.com/paper/2003.07278/) (2020)
- [Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments](https://mlsanity.com/paper/2006.16437/) (2020)
- [Paper Matching with Local Fairness Constraints](https://mlsanity.com/paper/1905.11924/) (2019)
- [PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space](https://mlsanity.com/paper/1909.11258/) (2019)
- [A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation](https://mlsanity.com/paper/1912.08976/) (2019)

## Built on by (most cited first, 4 of 4)

- [AgentReview: Exploring Peer Review Dynamics with LLM Agents](https://mlsanity.com/paper/2406.12708/) (2024)
- [exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment Problem](https://mlsanity.com/paper/2502.07683/) (2025)
- [Modeling LLM Agent Reviewer Dynamics in Elo-Ranked Review System](https://mlsanity.com/paper/2601.08829/) (2026)
- [Reimagining Peer Review Process Through Multi-Agent Mechanism Design](https://mlsanity.com/paper/2601.19778/) (2026)

## Abstract (arXiv)

Peer review is an integral component of scientific research. The quality of peer review, and consequently the published research, depends to a large extent on the ability to recruit adequate reviewers for submitted papers. However, finding such reviewers is an increasingly difficult task due to several factors, such as the continuous increase both in the production of scientific papers and the workload of scholars. To mitigate these challenges, solutions for automated association of papers with "well matching" reviewers - the task often referred to as reviewer assignment problem (RAP) - have been the subject of research for thirty years now. Even though numerous solutions have been suggested, to our knowledge, a recent systematic synthesis of the RAP-related literature is missing. To fill this gap and support further RAP-related research, in this paper, we present a scoping review of computational approaches for addressing RAP. Following the latest methodological guidance for scoping reviews, we have collected recent literature on RAP from three databases (Scopus, Google Scholar, DBLP) and, after applying the eligibility criteria, retained 26 studies for extracting and synthesising data on several aspects of RAP research including: i) the overall framing of and approach to RAP; ii) the criteria for reviewer selection; iii) the modelling of candidate reviewers and submissions; iv) the computational methods for matching reviewers and submissions; and v) the methods for evaluating the performance of the proposed solutions. The paper summarises and discusses the findings for each of the aforementioned aspects of RAP research and suggests future research directions.