An Introduction to Variational Methods for Graphical Models
Machine Learning
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
Training and testing of recommender systems on data missing not at random
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy
Proceedings of the fifth ACM conference on Recommender systems
A hierarchical model for ordinal matrix factorization
Statistics and Computing
Alternating least squares for personalized ranking
Proceedings of the sixth ACM conference on Recommender systems
Proceedings of the sixth ACM conference on Recommender systems
One-class collaborative filtering with random graphs
Proceedings of the 22nd international conference on World Wide Web
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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We present a matrix factorization model inspired by challenges we encountered while working on the Xbox movies recommendation system. The item catalog in a recommender system is typically equipped with meta-data features in the form of labels. However, only part of these features are informative or useful with regard to collaborative filtering. By incorporating a novel sparsity prior on feature parameters, the model automatically discerns and utilizes informative features while simultaneously pruning non-informative features. The model is designed for binary feedback, which is common in many real-world systems where numeric rating data is scarce or non-existent. However, the overall framework is applicable to any likelihood function. Model parameters are estimated with a Variational Bayes inference algorithm, which is robust to over-fitting and does not require cross-validation and fine tuning of regularization coefficients. The efficacy of our method is illustrated on a sample from the Xbox movies dataset as well as on the publicly available MovieLens dataset. In both cases, the proposed solution provides superior predictive accuracy, especially for long-tail items. We then demonstrate the feature selection capabilities and compare against the common case of simple Gaussian priors. Finally, we show that even without features, our model performs better than a baseline model trained with the popular stochastic gradient descent approach.