Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
A Bayesian approach toward active learning for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Generalized Probabilistic Matrix Factorizations for Collaborative Filtering
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Recommender Systems Handbook
Towards Optimal Active Learning for Matrix Factorization in Recommender Systems
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Active collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Active learning for online bayesian matrix factorization
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
On top-k recommendation using social networks
Proceedings of the sixth ACM conference on Recommender systems
Exploiting the characteristics of matrix factorization for active learning in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
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Collaborative prediction is a powerful technique, useful in domains from recommender systems to guiding the scientific discovery process. Low-rank matrix factorization is one of the most powerful tools for collaborative prediction. This work presents a general approach for active collaborative prediction with the Probabilistic Matrix Factorization model. Using variational approximations or Markov chain Monte Carlo sampling to estimate the posterior distribution over models, we can choose query points to maximize our understanding of the model, to best predict unknown elements of the data matrix, or to find as many "positive" data points as possible. We evaluate our methods on simulated data, and also show their applicability to movie ratings prediction and the discovery of drug-target interactions.