Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
One-Class Collaborative Filtering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
One-class collaborative filtering with random graphs
Proceedings of the 22nd international conference on World Wide Web
Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection
Proceedings of the 7th ACM conference on Recommender systems
Selecting content-based features for collaborative filtering recommenders
Proceedings of the 7th ACM conference on Recommender systems
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Project Sage is Microsoft's all-purpose recommender system, designed and deployed as an ultra-high scale cloud service. Sage focuses on both state of the art research and high scale robust implementation. In the research front, we demonstrate new pre-processing and cleaning techniques, a novel probabilistic matrix factorization model for implicit one-class data, and a relatively new evaluation framework. In the engineering front, we present a working service deployed on the Microsoft Azure cloud, which provides easy-to-use interfaces to integrate a recommendation service into any website.