Deep Learning-Based Recommender Systems Research Progress: A Bibliometric Analysis
Wenting Guo, Zhirong Yang · 2023
Deep Learning-Based Recommender Systems (DLRS) represent a prominent research area in the academic community. This paper aims to conduct a bibliometric and visualization analysis using the VOSviewer software, based on 1,435 DLRS-related publications retrieved from the Web of Science database. By analyzing the existing literature, this study investigates the quantity of DLRS papers, their research origins, affiliated institutions, and notable authors. The findings reveal a substantial and rapid growth trend in DLRS publications since 2016. Co-occurrence analysis uncovers five major research directions within this field: deep recommender systems, collaborative prediction models, neural network personalization, attention-based models in recommendation, and deep learning for recommender systems. Finally, this paper provides relevant recommendations for future research and practical applications of DLRS, addressing both researchers and industry professionals.