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 vapourliquid 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.

Constrained Matrix Factorization for Course Score Prediction Recommender system is widely used in ecommercial platforms to recommend users suitable items according to usersu0027s preferences. In recent years, an increasing amount of attention has been paid to the application of recommender system in education. There are many online learning systems that can recommend students suitable courses according to studentsu0027 learning performances. However, there are few universities using recommender system to recommend students suitable elective courses. It is generally known that students in higher grade take the courses earlier than those in lower grade. Therefore, the elective course scores of sophomores can be predicted by using the course score information from students of higher grades. However, the unbalanced distribution of courseenrollment data makes it hard to predict the scores of the courses that are in a low selection rate. Therefore, we propose a Constrained Matrix Factorization (ConMF) algorithm to predict sophomoresu0027 elective course scores, which integrates the course average score into the objective function so as to make up the prediction deviation caused by the unbalanced course selection rate and make more accurate prediction than the traditional Matrix Factorization (MF) approach. The experimental results show that our proposed model outperforms the stateoftheart methods in the task of university studentsu0027 course score prediction.