Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
The multiple multiplicative factor model for collaborative filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
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
Non-linear matrix factorization with Gaussian processes
ICML '09 Proceedings of the 26th Annual 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
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Prediction of social bookmarking based on a behavior transition model
Proceedings of the 2010 ACM Symposium on Applied Computing
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
The Journal of Machine Learning Research
A probe prediction approach to overlay network monitoring
Proceedings of the 7th International Conference on Network and Services Management
Kernel-Mapping Recommender system algorithms
Information Sciences: an International Journal
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
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
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Fast gradient-based methods for Maximum Margin Matrix Factorization (MMMF) were recently shown to have great promise (Rennie & Srebro, 2005), including significantly outperforming the previous state-of-the-art methods on some standard collaborative prediction benchmarks (including MovieLens). In this paper, we investigate ways to further improve the performance of MMMF, by casting it within an ensemble approach. We explore and evaluate a variety of alternative ways to define such ensembles. We show that our resulting ensembles can perform significantly better than a single MMMF model, along multiple evaluation metrics. In fact, we find that ensembles of partially trained MMMF models can sometimes even give better predictions in total training time comparable to a single MMMF model.