Exploring drawbacks in music recommender systems : the Spotify case

Currently, more and more people use music streaming websites to listen to music, and a music recommendation service is commonly provided on the music streaming websites. A good music recommender system improves people’s user experience of music streaming websites. Nevertheless, there are some issues regarding the existing music recommender systems that need to be looked into.The purpose of this thesis is to identify the weaknesses of music recommender systems. Spotify, a Swedish music streaming website, has a large number of users. As it is a widely known streaming service, it seems appropriate for a case study on the drawbacks of music recommender systems.The case study method has been chosen for doing this research. The process of making up this thesis was divided into three stages. At the first stage, some basic preparations for the thesis were done. The second stage was characterized by some empirical work, like interviews and questionnaires, to collect the required data. Those empirical findings were analyzed in the third part to help us to identify and define the drawbacks.The research results presented in this thesis contribute to close several knowledge gaps in the area of music recommender systems and could thus be beneficial to different actors: streaming website operators to identify drawbacks of their recommender system; designers of recommender systems to improve system design; and, last but not least, this thesis provides some useful advice to those who market music streaming websites.This thesis does not focus on the technical and algorithm fields, i.e. the hardware- and software-related background. Instead, the idea and the functions of the recommender system, its feedback loop and the user experience were subject to our research and discussion. The results of the thesis can provide those responsible with both and inspiration for creating more customized recommender systems