Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Video2Text: Learning to Annotate Video Content
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics
Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics
Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space
Unifying explicit and implicit feedback for collaborative filtering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Music recommendation and discovery revisited
Proceedings of the fifth ACM conference on Recommender systems
Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy
Proceedings of the fifth ACM conference on Recommender systems
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
Efficient top-n recommendation for very large scale binary rated datasets
Proceedings of the 7th ACM conference on Recommender systems
Evaluation in Music Information Retrieval
Journal of Intelligent Information Systems
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We introduce the Million Song Dataset Challenge: a large-scale, personalized music recommendation challenge, where the goal is to predict the songs that a user will listen to, given both the user's listening history and full information (including meta-data and content analysis) for all songs. We explain the taste profile data, our goals and design choices in creating the challenge, and present baseline results using simple, off-the-shelf recommendation algorithms.