Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Experiences with GroupLens: marking usenet useful again
ATEC '97 Proceedings of the annual conference on USENIX Annual Technical Conference
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
Finding geographically representative music via social media
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Hybrid recommenders: incorporating metadata awareness into latent factor models
Proceedings of the 19th Brazilian symposium on Multimedia and the web
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It has been shown in several studies that demographics such as gender, socio-economic background and age affect one's musical tastes. In this work we combine these factors with traditional collaborative filtering techniques in order to improve recommendation precision. We propose a simple measure for combining the data and show that it has potential for this application.