Andreu Vall, Marcin Skowron, Peter Knees, Markus Schedl,
"Improving Music Recommendations with a Weighted Factorization of the Tagging Activity"
: Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR),, 10-2015
Original Titel:
Improving Music Recommendations with a Weighted Factorization of the Tagging Activity
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR),
Original Kurzfassung:
Collaborative filtering systems for music recommendations
are often based on implicit feedback derived from listening
activity. Hybrid approaches further incorporate additional
sources of information in order to improve the quality of
the recommendations. In the context of a music streaming
service, we present a hybrid model based on matrix factorization
techniques that fuses the implicit feedback derived
from the users? listening activity with the tags that
users have given to musical items. In contrast to existing
work, we introduce a novel approach to exploit tags
by performing a weighted factorization of the tagging activity.
We evaluate the model for the task of artist recommendation,
using the expected percentile rank as metric,
extended with confidence intervals to enable the comparison
between models. Thus, our contribution is twofold:
(1) we introduce a novel model that uses tags to improve
music recommendations and (2) we extend the evaluation
methodology to compare the performance of different recommender
systems.