Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Controlled experiments on the web: survey and practical guide
Data Mining and Knowledge Discovery
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In this paper we propose a hybrid music recommender system, which combines usage and content data. We describe an online evaluation experiment performed in real time on a commercial music web site, specialised in content from the very long tail of music content. We compare it against two stand-alone recommenders, the first system based on usage and the second one based on content data. The results show that the proposed hybrid recommender shows advantages with respect to usage- and content-based systems, namely, higher user absolute acceptance rate, higher user activity rate and higher user loyalty.