Abstract: Recognizing user emotions is one of the most important factors for improving user satisfaction in music recommendation systems. This research explains emotion-based music recommendation systems, their use cases, and technologies involved in their creation. Relating to the growing size of digital music collections and increased access to streaming services, emotional understanding of music is important for providing emotionally relevant and personalized recommendations. This research analyzes the different emotion recognition methods in music, including acoustic, lyrics, and hybrid approaches, and discusses the effectiveness of these approaches. It analyzes the influence of emotional content on user engagement, user satisfaction, and playlist desirability. Moreover, we address issues of building efficient emotion-based recommenders, such as data annotation, cultural divergences of emotions, and emotion model interpretability. This research addresses the future of emotion-informed music recommendation systems, such as the fusion of physiological information or signals and modern machine learning methods. The overarching aim is to generate music recommendations for the user that are not only relevant according to their choices but also their feelings at the given moment. Thus, this paper answers the call regarding the development of music recommenders in the contemporary world.

Key words: physiological signals, machine learning, sentiment analysis, emotion context