An Approach to Analyzing Correlation between Songs/Artists Using iTMS Playlists

Digital audio devices have been changing music entertainment environment. Those devices are bundled with music jukebox software, such as Apple´s iTunes, Sony´s CONNECT player. Jukebox software not only enables us to recode, play, search, purchase music on PC, but also to manage playlist. Anybody can make his/her own playlists, and play music according to the list. In this paper, we focus on iTMS (iTunse Music Stroe) playlists and use them as the data mining resources for a music recommendation system, then developing correlation measuring methods. We have retrieved about 13,000 playlists, and analyzed the frequency of artists/songs, co-occurrence of artists/songs in the playlists. Through the result data, we found out that all graphs we drew are follow the Zipf´s law. Furthermore, we have analyzed the hierarchical relation between songs according to their popularity. We proposed a basic idea of popularity measuring method