A Systematic Review of Citation Recommendation Over the Past Two Decades

• Published in: International journal on semantic web and information systems Vol. 19; no. 1; pp. 1 - 22
• Main Authors: Lee, Lap-Kei, Liang, Yicong
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.01.2023
• Subjects: Analysis; Artificial neural networks; Neural networks; Recommender systems; Students; Systematic review
• ISSN: 1552-6283; 1552-6291
• DOI: 10.4018/IJSWIS.324071

A citation is a reference to the source of information used in an article. Citations are very useful for students and researchers to locate relevant information on a topic. Proper citation is also important in the academic ethics of article writing. Due to the rapid growth of scientific works published each year, how to automatically recommend citations to students and researchers has become an interesting but challenging research problem. In particular, a citation recommendation system can assist students to identify relevant papers and literature for academic writing. Citation recommendation can be classified into local and global citation recommendation depending on whether a specific local citation context is given; e.g., the text surrounding a citation placeholder. This article provides a systematic review on global citation recommendation models and compares the reviewed methods from the traditional topic- based models to the recent models embedded with deep neural networks, aiming to summarize this field to facilitate researchers working on citation recommendation.