An introduction to variational methods for graphical models
Learning in graphical models
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
The Journal of Machine Learning Research
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering for orkut communities: discovery of user latent behavior
Proceedings of the 18th international conference on World wide web
Non-linear matrix factorization with Gaussian processes
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Transfer learning for collaborative filtering via a rating-matrix generative model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Fast nonparametric matrix factorization for large-scale collaborative filtering
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Bayesian latent variable models for collaborative item rating prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Theoretical Analysis of Bayesian Matrix Factorization
The Journal of Machine Learning Research
Multi-task Learning for Bayesian Matrix Factorization
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Signal Modeling and Classification Using a Robust Latent Space Model Based on Distributions
IEEE Transactions on Signal Processing
TALMUD: transfer learning for multiple domains
Proceedings of the 21st ACM international conference on Information and knowledge management
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The dramatic rates new digital content becomes available has brought collaborative filtering systems to the epicenter of computer science research in the last decade. One of the greatest challenges collaborative filtering systems are confronted with is the data sparsity problem: users typically rate only very few items; thus, availability of historical data is not adequate to effectively perform prediction. To alleviate these issues, in this paper we propose a novel multitask collaborative filtering approach. Our approach is based on a coupled latent factor model of the users rating functions, which allows for coming up with an agile information sharing mechanism that extracts much richer task-correlation information compared to existing approaches. Formulation of our method is based on concepts from the field of Bayesian nonparametrics, specifically Indian Buffet Process priors, which allow for data-driven determination of the optimal number of underlying latent features (item characteristics and user traits) assumed in the context of the model. We experiment on several real-world datasets, demonstrating both the efficacy of our method, and its superiority over existing approaches.