RECOMMENDATION SYSTEM FOR MUSIC BASED ON CONTENT AND POPULARITY RATINGS

Abstract

Digital music is more accessible than ever before, thanks to commercial music streaming services accessible through mobile devices. Organizing all of this digital music takes a long time and causes information overload. As a result, creating a music recommender system that can automatically scan your music library and recommend the appropriate songs to your consumers is quite beneficial. Music providers can employ a music recommender system to anticipate and deliver relevant songs to users based on the qualities of previously listened music. Because the availability of digital music is so vast today compared to the past period, sorting all of it takes a long time and produces information fatigue. Be it buying a kindle book, selecting music on Netflix, or studying an essay on Medium, recommender systems have shaped our online choices. They are, though, still in the early stages of development and far from ideal. We explore Music recommender systems in particular, as well as numerous types of recommendation strategies and the issues they encounter, in this study. We also attempt to critically evaluate some work on music recommendation systems and explore various research articles that have aided in the resolution of numerous issues that these systems have faced. Despite these advancements, recommender systems must still be developed to a greater level in order to be more successful in giving correct suggestions on a wide range of topics. The paper discusses how to create a music recommendation system, as well as different methodologies, alternative ways that might be employed, and future developments.