Natural gradient works efficiently in learning
Neural Computation
An Introduction to Variational Methods for Graphical Models
Machine Learning
Online Model Selection Based on the Variational Bayes
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The evidence framework applied to classification networks
Neural Computation
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
One-Class Collaborative Filtering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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
A hierarchical model for ordinal matrix factorization
Statistics and Computing
Proceedings of the sixth ACM conference on Recommender systems
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
Sage: recommender engine as a cloud service
Proceedings of the 7th ACM conference on Recommender systems
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The bane of one-class collaborative filtering is interpreting and modelling the latent signal from the missing class. In this paper we present a novel Bayesian generative model for implicit collaborative filtering. It forms a core component of the Xbox Live architecture, and unlike previous approaches, delineates the odds of a user disliking an item from simply being unaware of it. The latent signal is treated as an unobserved random graph connecting users with items they might have encountered. We demonstrate how large-scale distributed learning can be achieved through a combination of stochastic gradient descent and mean field variational inference over random graph samples. A fine-grained comparison is done against a state of the art baseline on real world data.