Putting the User in the Center of Music Information Retrieval

Personalized and context-aware music retrieval and recommendation algorithms ideally provide music that perfectly fits the individual listener in each imaginable situation and for each of her information or entertainment need. Although first steps towards such systems have recently been presented at ISMIR and similar venues, this vision is still far away from being a reality. In this paper, we investigate and discuss literature on the topic of user-centric music retrieval and reflect on why the breakthrough in this field has not been achieved yet. Given the different expertises of the authors, we shed light on why this topic is a particularly challenging one, taking a psychological and a computer science view. Whereas the psychological point of view is mainly concerned with proper experimental design, the computer science aspect centers on modeling and machine learning problems. We further present our ideas on aspects vital to consider when elaborating user-aware music retrieval systems, and we also describe promising evaluation methodologies, since accurately evaluating personalized systems is a notably challenging task.