Understanding users music listening habits for time and activity sensitive customized playlists

Music playlists have rapidly become one of the top services of streaming platforms: users do not need to spend time into deciding what to listen to, they just have to select the proper pre-compiled playlists composed by some music they know and some new songs close to their taste. However, users will continue to listen to playlists only if these conform to their musical tastes. Indeed, users listening habits might differ according to the activity they are performing while listening to music, and suggested playlists should reflect that. As there still are not in literature standard methods to produce customized playlists automatically, in this work in progress paper we focus on how to exploit users past music hearings to define genre listening habits in specific days of the week and hours of the day. We present our first investigation toward this direction by proposing a simple and computationally inexpensive method and by testing it using the Spotify listening history of volunteer users. We show that the method is promising and discuss several future working directions to improve the method and make it more effective.