MUSIC RECOMMENDATION SYSTEM

Abstract

As the accessibility of numerous music music recommender became streaming services has been extended, the more and more relevant. Many oi these streaming services, such as Spotify, have their advances in own lecommendation system. Despite several recommendation techniques, systems recommendations are usually still not correct. This paper provides music data collection from recommendation as a an overview of the history and developments of a high content. This thesis also describes the backlog and the methodology ofmusic recommendations by content options and content simulations pioviding detailed descriptions of sound used in music content advisors. Many of the measuring options granted for our own content Objective and subjective analysis of the researchers ' results that the music advice based offer the most correct guidance. enforced system further ensures the on audio content alone does not In order to make sure that advice is recommendation is listed as factors, this thesis focuses recommendation and describes certain used in content-based recommender discussed. The history and development of the correctly defined as a problem and ♦ on the content-based music key audio options and similarity measures music are recovery analytics in Chapter Three, is summarized shortly. systems. The challenges related to music data management with the guideline for target content is discussed An analysis of alternative recommendation techniques