A Bibliometric Analysis of Recommender Systems

This study conducts a bibliometric analysis of research on Recommender Systems that will be noted (RS) to reveal the most important models, topics, and publications in this field. We conducted the research from 2019 to 2023. We evaluated the data from the Scopus database by using keywords that relate to RS methodologies, applications, and fields under development. The search results yielded 87759 documents, which were subsequently loaded into the VOSviewer software. Using VOSviewer, we analyzed co-citation patterns, keyword frequency, author/institution collaboration networks, and citation analysis. High-frequency keywords indicate a growing interest in the field of RS. The study identifies He Xiangnan, Zhang Yongfeng, Yin Hongzhi, Yu Philip S., Tang Ruiming, and Jannach Dietmar as the leading researchers in this subject. In addition, citation analysis shows that computer science, medicine, engineering, social sciences, mathematics, and decision-making are at the top of the RS research fields. The results of this research can help researchers discover what is new in recommender systems.