Proceedings of the 1998 conference on Advances in neural information processing systems II
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
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Applying collaborative filtering techniques to movie search for better ranking and browsing
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
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
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
Overlay management for fully distributed user-based collaborative filtering
EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
Generalizing matrix factorization through flexible regression priors
Proceedings of the fifth ACM conference on Recommender systems
A hierarchical model for ordinal matrix factorization
Statistics and Computing
Kernel-Mapping Recommender system algorithms
Information Sciences: an International Journal
Learning multiple-question decision trees for cold-start recommendation
Proceedings of the sixth ACM international conference on Web search and data mining
Simultaneous particle tracking in multi-action motion models with synthesized paths
Image and Vision Computing
Nonparametric guidance of autoencoder representations using label information
The Journal of Machine Learning Research
PREA: personalized recommendation algorithms toolkit
The Journal of Machine Learning Research
Learning output kernels for multi-task problems
Neurocomputing
Nonparametric bayesian multitask collaborative filtering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Nonlinear latent factorization by embedding multiple user interests
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 23rd international conference on World wide web
Modeling contextual agreement in preferences
Proceedings of the 23rd international conference on World wide web
| Hi-index | 0.00 |
A popular approach to collaborative filtering is matrix factorization. In this paper we develop a non-linear probabilistic matrix factorization using Gaussian process latent variable models. We use stochastic gradient descent (SGD) to optimize the model. SGD allows us to apply Gaussian processes to data sets with millions of observations without approximate methods. We apply our approach to benchmark movie recommender data sets. The results show better than previous state-of-the-art performance.