A Study on Developing Machine Learning-based Journal Recommendation System Using Language Models: Focusing on the Scholarly Publishing Ecosystem of South Korea (언어모델을 활용한 기계학습 기반의 학술지 추천 모형 개발 - 국내 학술출판 생태계를 중심으로 -)

Jaemin Chung (정재민); Eunkyung Nam (남은경); Wan Jong Kim (김완종)

doi:10.14699/KBIBLIA.2025.36.1.null.55347

A Study on Developing Machine Learning-based Journal Recommendation System Using Language Models: Focusing on the Scholarly Publishing Ecosystem of South Korea

Journal of the Korean Biblia Society for Library and Information Science
2025, 36(1), pp.109~126
DOI : 10.14699/KBIBLIA.2025.36.1.null.55347
Publisher : Journal Of The Korean Biblia Society For Library And Information Science
Research Area : Interdisciplinary Studies > Library and Information Science
Received : February 14, 2025
Accepted : February 28, 2025
Published : March 30, 2025

Jaemin Chung ¹, Eunkyung Nam ², Wan Jong Kim ²

¹한국과학기술정보연구원 데이터큐레이션센터
²한국과학기술정보연구원

Accredited

ABSTRACT

As interdisciplinary research expands through the convergence of academic fields and the number of accessible electronic journals increases, researchers face growing challenges in selecting appropriate journals for manuscript submission. There is a lack of research on journal recommendation systems that reflect the Korean academic ecosystem, in which academic services offer different sets of journals and international journals published by Korean academic societies are increasing. This study proposes a machine learning-based journal recommendation architecture that leverages language models. The proposed architecture embeds paper titles and abstracts using BERT-based language models further trained on target data, and these embedded vectors are then input into an XGBoost classifier to recommend appropriate journals. Analysis results showed that among BERT-based models, RoBERTa demonstrated the best performance, with its recommendation system outperforming approximately 13% higher compared to systems based on traditional natural language processing techniques. Furthermore, it was found that recommendations for papers outside the scope of service journals and papers written in Korean were feasible. This study contributes both academically and practically by presenting an academic journal recommendation architecture that leverages language models and machine learning by considering the actual Korean academic publishing environment.

KEYWORDS

Journal Recommendation, Language Model, Document Embedding, Machine Learning, Multi-Class Classification

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