A unifying review of linear Gaussian models
Neural Computation
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)
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Large-scale collaborative prediction using a nonparametric random effects model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce
Proceedings of the 19th international conference on World wide web
Location recommendation for location-based social networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
CMAP: effective fusion of quality and relevance for multi-criteria recommendation
Proceedings of the fourth ACM international conference on Web search and data mining
Learning to recommend with explicit and implicit social relations
ACM Transactions on Intelligent Systems and Technology (TIST)
Limitations of matrix completion via trace norm minimization
ACM SIGKDD Explorations Newsletter
Probabilistic factor models for web site recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Handling data sparsity in collaborative filtering using emotion and semantic based features
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
An exploration of improving collaborative recommender systems via user-item subgroups
Proceedings of the 21st international conference on World Wide Web
A hierarchical model for ordinal matrix factorization
Statistics and Computing
Memory-restricted latent semantic analysis to accumulate term-document co-occurrence events
Pattern Recognition Letters
Predicting the ratings of multimedia items for making personalized recommendations
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Constrained collective matrix factorization
Proceedings of the sixth ACM conference on Recommender systems
Regularized nonnegative shared subspace learning
Data Mining and Knowledge Discovery
Learning multiple-question decision trees for cold-start recommendation
Proceedings of the sixth ACM international conference on Web search and data mining
An experimental study on implicit social recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Distributed large-scale natural graph factorization
Proceedings of the 22nd international conference on World Wide Web
PREA: personalized recommendation algorithms toolkit
The Journal of Machine Learning Research
Nonparametric bayesian multitask collaborative filtering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Instant foodie: predicting expert ratings from grassroots
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Partial-update dimensionality reduction for accumulating co-occurrence events
Pattern Recognition Letters
Cross domain recommendation based on multi-type media fusion
Neurocomputing
Colbar: A collaborative location-based regularization framework for QoS prediction
Information Sciences: an International Journal
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With the sheer growth of online user data, it becomes challenging to develop preference learning algorithms that are sufficiently flexible in modeling but also affordable in computation. In this paper we develop nonparametric matrix factorization methods by allowing the latent factors of two low-rank matrix factorization methods, the singular value decomposition (SVD) and probabilistic principal component analysis (pPCA), to be data-driven, with the dimensionality increasing with data size. We show that the formulations of the two nonparametric models are very similar, and their optimizations share similar procedures. Compared to traditional parametric low-rank methods, nonparametric models are appealing for their flexibility in modeling complex data dependencies. However, this modeling advantage comes at a computational price--it is highly challenging to scale them to large-scale problems, hampering their application to applications such as collaborative filtering. In this paper we introduce novel optimization algorithms, which are simple to implement, which allow learning both nonparametric matrix factorization models to be highly efficient on large-scale problems. Our experiments on EachMovie and Netflix, the two largest public benchmarks to date, demonstrate that the nonparametric models make more accurate predictions of user ratings, and are computationally comparable or sometimes even faster in training, in comparison with previous state-of-the-art parametric matrix factorization models.