Investigating the Role of Personalization When Creating Relaxing Playlists - Mélo, Daniel (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia), 2020)
More than just a common habit or pastime of people, listening to music is often used as a way to facilitate listeners to achieve a certain mental state or to perform specific activities. Among the most common activities performed when listening to music is relaxation. According to findings from the area of music therapy, a good musical choice for relaxation needs personalization. However, until now, many studies in the area of Music Information Retrieval (MIR) have focused on only identifying general characteristics of music according to certain contexts. Thus, this paper focuses on expanding the findings of music therapy, considering aspects of musicology and a large amount of data. To this end, we: (i) explore more than 60 thousand handcrafted playlists of almost 5 thousand users; (ii) create a user preference model based on high and low-level features of the songs and (iii) analyze the differences between the users' preferences. The results of our analysis suggest that personalization plays a key role in the playlists created by the users. With this work, we want to pave the way and encourage the improvement of context-aware music recommender systems or other MIR concepts like user behavior analysis.