Impact of Listening Behavior on Music Recommendation
Sprache des Vortragstitels:
Englisch
Original Kurzfassung:
The next generation of music recommendation systems will
be increasingly intelligent and likely take into account user
behavior for more personalized recommendations. In this
work we consider user behavior when making recommendations
with features extracted from a user?s history of listening
events. We investigate the impact of listener?s behavior
by considering features such as play counts, ?mainstreaminess?,
and diversity in music taste on the performance
of various music recommendation approaches. The
underlying dataset has been collected by crawling social
media (specifically Twitter) for listening events. Each user?s
listening behavior is characterized into a three dimensional
feature space consisting of play count, ?mainstreaminess?
(i.e. the degree to which the observed user listens to currently
popular artists), and diversity (i.e. the diversity of
genres the observed user listens to). Drawing subsets of
the 28,000 users in our dataset, according to these three
dimensions, we evaluate whether these dimensions influence
figures of merit of various music recommendation approaches,
in particular, collaborative filtering (CF) and CF
enhanced by cultural information such as users located in
the same city or country.