Learning recommender systems with adaptive regularization

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.