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

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

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 tha...

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Bibliographic Details
Main Authors: Mohammed Hassan, Mohamed Hamada
Format: Article
Language:English
Published: Atlantis Press 2018-01-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Multi-criteria recommender systems
Genetic algorithms
Aggregation function
Evaluation metrics
Prediction accuracy
Online Access:https://www.atlantis-press.com/article/25885050/view
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spelling doaj-0da907a2144b44238f9595ea701565be2020-11-25T02:06:04ZengAtlantis PressInternational Journal of Computational Intelligence Systems 1875-68832018-01-0111110.2991/ijcis.11.1.12Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender SystemsMohammed HassanMohamed HamadaWe 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.https://www.atlantis-press.com/article/25885050/viewMulti-criteria recommender systemsGenetic algorithmsAggregation functionEvaluation metricsPrediction accuracy
collection DOAJ
language English
format Article
sources DOAJ
author Mohammed Hassan
Mohamed Hamada
spellingShingle Mohammed Hassan
Mohamed Hamada
Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems
International Journal of Computational Intelligence Systems
Multi-criteria recommender systems
Genetic algorithms
Aggregation function
Evaluation metrics
Prediction accuracy
author_facet Mohammed Hassan
Mohamed Hamada
author_sort Mohammed Hassan
title Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems
title_short Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems
title_full Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems
title_fullStr Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems
title_full_unstemmed Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems
title_sort genetic algorithm approaches for improving prediction accuracy of multi-criteria recommender systems
publisher Atlantis Press
series International Journal of Computational Intelligence Systems
issn 1875-6883
publishDate 2018-01-01
description 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.
topic Multi-criteria recommender systems
Genetic algorithms
Aggregation function
Evaluation metrics
Prediction accuracy
url https://www.atlantis-press.com/article/25885050/view
work_keys_str_mv AT mohammedhassan geneticalgorithmapproachesforimprovingpredictionaccuracyofmulticriteriarecommendersystems
AT mohamedhamada geneticalgorithmapproachesforimprovingpredictionaccuracyofmulticriteriarecommendersystems
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