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

Juan A. Gómez-Pulido, Enrique Cortés-Toro, Arturo Durán-Domínguez, Broderick Crawford, Ricardo Soto

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaIntelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
EditoresHujun Yin, Paulo Novais, David Camacho, Antonio J. Tallón-Ballesteros
EditorialSpringer Verlag
Páginas125-133
Número de páginas9
ISBN (versión impresa)9783030034924
DOI
EstadoPublicada - 2018
Publicado de forma externa
Evento19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - Madrid, Espana
Duración: 21 nov. 201823 nov. 2018

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11314 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
País/TerritorioEspana
CiudadMadrid
Período21/11/1823/11/18

Huella

Profundice en los temas de investigación de 'Novel and Classic Metaheuristics for Tunning a Recommender System for Predicting Student Performance in Online Campus'. En conjunto forman una huella única.

Citar esto