Comparison between stochastic gradient descent and VLE metaheuristic for optimizing matrix factorization

Matrix factorization is used by recommender systems in collaborative filtering for building prediction models based on a couple of matrices. These models are usually generated by stochastic gradient descent algorithm, which learns the model minimizing the error done. Finally, the obtained models are validated according to an error criterion by predicting test data. Since the model generation can be tackled as an optimization problem where there is a huge set of possible solutions, we propose to use metaheuristics as alternative solving methods for matrix factorization. In this work we applied a novel metaheuristic for continuous optimization, which works inspired by the vapour-liquid equilibrium. We considered a particular case were matrix factorization was applied: the prediction student performance problem. The obtained results surpassed thoroughly the accuracy provided by stochastic gradient descent.