A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Adaptive Regularization in Neural Network Modeling
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
The Entire Regularization Path for the Support Vector Machine
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
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
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
Retweet or not?: personalized tweet re-ranking
Proceedings of the sixth ACM international conference on Web search and data mining
Exploiting ranking factorization machines for microblog retrieval
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Many factorization models like matrix or tensor factorization have been proposed for the important application of recommender systems. The success of such factorization models depends largely on the choice of good values for the regularization parameters. Without a careful selection they result in poor prediction quality as they either underfit or overfit the data. Regularization values are typically determined by an expensive search that requires learning the model parameters several times: once for each tuple of candidate values for the regularization parameters. In this paper, we present a new method that adapts the regularization automatically while training the model parameters. To achieve this, we optimize simultaneously for two criteria: (1) as usual the model parameters for the regularized objective and (2) the regularization of future parameter updates for the best predictive quality on a validation set. We develop this for the generic model class of Factorization Machines which subsumes a wide variety of factorization models. We show empirically, that the advantages of our adaptive regularization method compared to expensive hyperparameter search do not come to the price of worse predictive quality. In total with our method, learning regularization parameters is as easy as learning model parameters and thus there is no need for any time-consuming search of regularization values because they are found on-the-fly. This makes our method highly attractive for practical use.