Learning to learn with the informative vector machine
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
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Collaborative ordinal regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th 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
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
When recommendation meets mobile: contextual and personalized recommendation on the go
Proceedings of the 13th international conference on Ubiquitous computing
Tensor factorization using auxiliary information
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
A hierarchical model for ordinal matrix factorization
Statistics and Computing
Latent factor models with additive and hierarchically-smoothed user preferences
Proceedings of the sixth ACM international conference on Web search and data mining
Social trust prediction using heterogeneous networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
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A nonparametric model is introduced that allows multiple related regression tasks to take inputs from a common data space. Traditional transfer learning models can be inappropriate if the dependence among the outputs cannot be fully resolved by known input-specific and task-specific predictors. The proposed model treats such output responses as conditionally independent, given known predictors and appropriate unobserved random effects. The model is nonparametric in the sense that the dimensionality of random effects is not specified a priori but is instead determined from data. An approach to estimating the model is presented uses an EM algorithm that is efficient on a very large scale collaborative prediction problem. The obtained prediction accuracy is competitive with state-of-the-art results.