Technical Reports - Query Results
Your query term was 'number = 2012-10'1 report found
- OFAI-TR-2012-10 (
190kB PDF file)Putting the User in the Center of Music Information Retrieval
- Markus Schedl, Arthur Flexer
- 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.
Keywords: Music Information Retrieval, Evaluation, User studies
- 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.