Markus Schedl, Andreu Vall, Katayoun Farrahi,
"User Geospatial Context for Music Recommendation in Microblogs"
: Proceedings of the 37th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2014), Gold Coast, Australia, 2014
User Geospatial Context for Music Recommendation in Microblogs
Sprache des Titels:
Proceedings of the 37th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2014), Gold Coast, Australia
Music information retrieval and music recommendation are
seeing a paradigm shift towards methods that incorporate
user context aspects. However, structured experiments on a
standardized music dataset to investigate the effects of doing
so are scarce. In this paper, we compare performance of various
combinations of collaborative filtering and geospatial as
well as cultural user models for the task of music recommendation.
To this end, we propose a geospatial model that uses
GPS coordinates and a cultural model that uses semantic locations
(continent, country, and state of the user). We conduct
experiments on a novel standardized music collection,
the ?Million Musical Tweets Dataset? of listening events extracted
from microblogs. Overall, we find that modeling listeners?
location via Gaussian mixture models and computing
similarities from these outperforms both cultural user models
and collaborative filtering.