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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Autor*in:	Mohammed Hassan [verfasserIn] Mohamed Hamada [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

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

Übergeordnetes Werk:	In: International Journal of Computational Intelligence Systems - Springer, 2017, 11(2018), 1
Übergeordnetes Werk:	volume:11 ; year:2018 ; number:1

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.2991/ijcis.11.1.12

Katalog-ID:	DOAJ04044757X

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allfieldsSound	10.2991/ijcis.11.1.12 doi (DE-627)DOAJ04044757X (DE-599)DOAJ0da907a2144b44238f9595ea701565be DE-627 ger DE-627 rakwb eng QA75.5-76.95 Mohammed Hassan verfasserin aut Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Multi-criteria recommender systems Genetic algorithms Aggregation function Evaluation metrics Prediction accuracy Electronic computers. Computer science Mohamed Hamada verfasserin aut In International Journal of Computational Intelligence Systems Springer, 2017 11(2018), 1 (DE-627)777781514 (DE-600)2754752-8 18756883 nnns volume:11 year:2018 number:1 https://doi.org/10.2991/ijcis.11.1.12 kostenfrei https://doaj.org/article/0da907a2144b44238f9595ea701565be kostenfrei https://www.atlantis-press.com/article/25885050/view kostenfrei https://doaj.org/toc/1875-6883 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 1
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topic	misc QA75.5-76.95 misc Multi-criteria recommender systems misc Genetic algorithms misc Aggregation function misc Evaluation metrics misc Prediction accuracy misc Electronic computers. Computer science
topic_unstemmed	misc QA75.5-76.95 misc Multi-criteria recommender systems misc Genetic algorithms misc Aggregation function misc Evaluation metrics misc Prediction accuracy misc Electronic computers. Computer science
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title	Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems
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title_auth	Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems
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.
abstractGer	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.
abstract_unstemmed	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.
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