The Journal of Machine Learning Research
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
Typology of mixed-membership models: towards a design method
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Algorithms for probabilistic latent tensor factorization
Signal Processing
Improving performance of topic models by variable grouping
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Cross-domain collaborative filtering over time
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Distributed Monitoring with Collaborative Prediction
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Learning bi-clustered vector autoregressive models
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Modeling user's receptiveness over time for recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Efficient distributed monitoring with active Collaborative Prediction
Future Generation Computer Systems
Discovering different types of topics: factored topic models
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
CoBaFi: collaborative bayesian filtering
Proceedings of the 23rd international conference on World wide web
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Matrix factorization algorithms are frequently used in the machine leaming community to find low dimensional representations of data. We introduce a novel generative Bayesian probabilistic model for unsupervised matrix and tensor factorization. The model consists of several interacting LDA models, one for each modality. We describe an efficient collapsed Gibbs sampler for inference. We also derive the non-parametric form of the model where interacting LDA models are replaced with interacting HDP models. Experiments demonstrate that the model is useful for prediction of missing data with two or more modalities as well as learning the latent structure in the data.