Novel and Classic Metaheuristics for Tunning a Recommender System for Predicting Student Performance in Online Campus

Abstract

The prediction of the students’ performance allows to improve the learning process using the online campus tools. In this context, recommender systems are useful for prediction purposes. This collaborative filtering tool, predicts the unknown performances analyzing the database that contains the performance of the students for particular tasks, considering matrix factorization and stochastic gradient descent. If we consider a fixed number of latent factors, the prediction error is mainly influenced by two parameters: learning rate and regularization factor. The best settings for these parameters is an optimization problem that can be tackled by soft computing techniques. In this work, we analyze three solving methods to select the optimal values of both parameters: a simple direct search, a classic evolutionary algorithm, and a novel metaheuristic. The results show the advantages of using metaheuristics instead of direct search in accuracy and computing effort terms.