Journal of the Korean Society for Library and Information Science
- P-ISSN1225-598X
- E-ISSN2982-6292
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A Systematic Review of Deep Learning-Based Article Recommendation Research
Abstract
Finding relevant research papers for citation has become increasingly challenging due to information overload, leading to growing interest in research paper recommendation systems. Over the past decade, deep learning methods have been employed to enhance the quality of paper recommendations. However, there has been a lack of systematic research on deep learning-based paper recommendation systems since 2020. This study conducts a systematic literature review of 47 deep learning-based research paper recommendation studies published between 2020 and 2023. The analysis examines data factors, data representation methods, methodologies, recommendation types, encountered challenges, and personalization aspects. Furthermore, it compares these studies with pre-2020 research by summarizing recommendation techniques, utilized datasets, deep learning methods, and key research topics by year. Based on this comparison, key insights are derived. Additionally, future research directions are suggested based on the analyzed research trends.
- keywords
- Research Paper Recommendation, Deep Learning, Systematic Literature Review, Recommendation Algorithms, Research Paper Recommendation Service