| Summary: | The study of Music Recommender Systems (MRS) has become crucial in digital music consumption, influencing how people discover and interact with music. This comprehensive analysis examines the complex field of MRS research from 2005 to 2023; with the growing importance of music recommendation systems in enhancing user experience, it is crucial to comprehend their development. By utilising rigorous social network analysis, statistical measures, and factor analysis, our investigation not only identifies essential themes and influential contributors but also emphasises the complex and diverse nature of MRS. The field trend significantly increased between 2017 and 2021, with periodic oscillations highlighting its dynamic nature. This analysis offers a broad perspective by examining highly cited articles, current researchers, and local sources. Factorial analysis uncovers thematic clusters, highlighting collaborative filtering, user experience, emotion identification, and reinforcement learning. A scientific mapping analysis classifies research themes in different historical periods, focusing on essential areas such as collaborative filtering, hybrid recommendation, sentiment analysis, and emotion identification. A review of thematic evolution highlights the importance of digitalisation, emotion recognition, personalisation, user experience, and collaborative filtering in determining future research directions. Although there has been a recent decrease in general interest, investigating context-aware models and hybrid techniques offers encouraging opportunities for further inquiry. This research enhances our comprehension of MRS dynamics, leading to future improvements and developments in the field. Ultimately, it improves the music discovery experience for people globally.
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