Automatic Reviewers Assignment to a Research Paper Based on Allied References and Publications Weight

2025-06-26

AI Summary

To address the challenge of ensuring domain expertise alignment in reviewer assignment amid the rapid proliferation of papers in emerging research areas, this paper proposes an automated reviewer recommendation method that jointly leverages citation networks and weighted academic impact metrics. The method innovatively integrates co-occurrence statistics of cited authors, dynamically weighted ranking based on multidimensional scholarly indicators (h-index, i10-index, and citation count), structured information extraction from author homepages, and collaborative relationship filtering. It encompasses a pipeline comprising web crawling, keyword extraction, academic metric parsing, and collaborative deduplication. Evaluated on a real-world paper dataset, the approach significantly improves recommendation relevance, precision, and domain specificity. It enables end-to-end automated reviewer selection and provides a scalable, high-accuracy solution for scholarly publishing peer-review workflows.

Technology Category

Data Mining & Knowledge Management: Recommender SystemsApplication Domains: Humanities & Computational Social ScienceSearch and Optimization: Metareasoning and Metaheuristics

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingGraph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Everyday, a vast stream of research documents is submitted to conferences, anthologies, journals, newsletters, annual reports, daily papers, and various periodicals. Many such publications use independent external specialists to review submissions. This process is called peer review, and the reviewers are called referees. However, it is not always possible to pick the best referee for reviewing. Moreover, new research fields are emerging in every sector, and the number of research papers is increasing dramatically. To review all these papers, every journal assigns a small team of referees who may not be experts in all areas. For example, a research paper in communication technology should be reviewed by an expert from the same field. Thus, efficiently selecting the best reviewer or referee for a research paper is a big challenge. In this research, we propose and implement program that uses a new strategy to automatically select the best reviewers for a research paper. Every research paper contains references at the end, usually from the same area. First, we collect the references and count authors who have at least one paper in the references. Then, we automatically browse the web to extract research topic keywords. Next, we search for top researchers in the specific topic and count their h-index, i10-index, and citations for the first n authors. Afterward, we rank the top n authors based on a score and automatically browse their homepages to retrieve email addresses. We also check their co-authors and colleagues online and discard them from the list. The remaining top n authors, generally professors, are likely the best referees for reviewing the research paper.

Problem

• Automatically assign best reviewers for research papers
• Match reviewers using paper references and author metrics
• Filter experts by h-index, citations, and topic relevance

Innovation

• Extract research topic keywords automatically
• Rank authors by h-index and citations
• Retrieve email addresses from homepages