  The task of a music recommender system is to predict what music item a
particular user would like to listen to next. This position paper discusses the
main challenges of the music preference prediction task: the lack of
information on the many contextual factors influencing a user's music
preferences in existing open datasets, the lack of clarity of what the right
choice of music is and whether a right choice exists at all; the multitude of
criteria (beyond accuracy) that have to be met for a "good" music item
recommendation; and the need for explanations on relationships to identify (and
potentially counteract) unwanted biases in recommendation approaches. The paper
substantiates the position that the confluence of theoretical modeling (which
seeks to explain behaviors) and algorithmic modeling (which seeks to predict
behaviors) seems to be an effective avenue to take in computational modeling
for music recommender systems.
