Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

DOI

10.2991/ijcis.11.1.12How to use a DOI?

Keywords

Multi-criteria recommender systems; Genetic algorithms; Aggregation function; Evaluation metrics; Prediction accuracy

Abstract

We often make decisions on the things we like, dislike, or even don’t care about. However, taking the right decisions becomes relatively difficult from a variety of items from different sources. Recommender systems are intelligent decision support software tools that help users to discover items that might be of interest to them. Various techniques and approaches have been applied to design and implement such systems to generate credible recommendations to users. A multi-criteria recommendation technique is an extended approach for modeling user’s preferences based on several characteristics of the items. This research presents genetic algorithm-based approaches for predicting user preferences in multi-criteria recommendation problems. Three genetic algorithms’ methods, namely standard genetic algorithm, adaptive genetic algorithm, and multi-heuristic genetic algorithms are used to conduct the experiments using a multi-criteria dataset for movies recommendation. The empirical results of the comparative analysis of their performance are presented in this study.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Mohammed Hassan
AU  - Mohamed Hamada
PY  - 2018
DA  - 2018/01/01
TI  - Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems
JO  - International Journal of Computational Intelligence Systems
SP  - 146
EP  - 162
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.12
DO  - 10.2991/ijcis.11.1.12
ID  - Hassan2018
ER  -