Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

Accuracy improvement is among the primary key research focuses in the area of recommender systems. Traditionally, recommender systems work on two sets of entities, Users and Items, to estimate a single rating that represents a user’s acceptance of an item. This technique was later extended to multi-...
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Autor*in:	Mohammed Hassan [verfasserIn] Mohamed Hamada [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	recommender systems artificial neural network genetic algorithm simulated annealing back-propagation Adaline Levenberg-Marquardt

Übergeordnetes Werk:	In: Computation - MDPI AG, 2014, 5(2017), 3, p 40
Übergeordnetes Werk:	volume:5 ; year:2017 ; number:3, p 40

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/computation5030040

Katalog-ID:	DOAJ054172179

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container_title	Computation
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callnumber-first	Q - Science
author	Mohammed Hassan
spellingShingle	Mohammed Hassan misc QA75.5-76.95 misc recommender systems misc artificial neural network misc genetic algorithm misc simulated annealing misc back-propagation misc Adaline misc Levenberg-Marquardt misc Electronic computers. Computer science Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems
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topic_title	QA75.5-76.95 Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems recommender systems artificial neural network genetic algorithm simulated annealing back-propagation Adaline Levenberg-Marquardt
topic	misc QA75.5-76.95 misc recommender systems misc artificial neural network misc genetic algorithm misc simulated annealing misc back-propagation misc Adaline misc Levenberg-Marquardt misc Electronic computers. Computer science
topic_unstemmed	misc QA75.5-76.95 misc recommender systems misc artificial neural network misc genetic algorithm misc simulated annealing misc back-propagation misc Adaline misc Levenberg-Marquardt misc Electronic computers. Computer science
topic_browse	misc QA75.5-76.95 misc recommender systems misc artificial neural network misc genetic algorithm misc simulated annealing misc back-propagation misc Adaline misc Levenberg-Marquardt misc Electronic computers. Computer science
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title	Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems
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title_sort	performance comparison of feed-forward neural networks trained with different learning algorithms for recommender systems
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title_auth	Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems
abstract	Accuracy improvement is among the primary key research focuses in the area of recommender systems. Traditionally, recommender systems work on two sets of entities, Users and Items, to estimate a single rating that represents a user’s acceptance of an item. This technique was later extended to multi-criteria recommender systems that use an overall rating from multi-criteria ratings to estimate the degree of acceptance by users for items. The primary concern that is still open to the recommender systems community is to find suitable optimization algorithms that can explore the relationships between multiple ratings to compute an overall rating. One of the approaches for doing this is to assume that the overall rating as an aggregation of multiple criteria ratings. Given this assumption, this paper proposed using feed-forward neural networks to predict the overall rating. Five powerful training algorithms have been tested, and the results of their performance are analyzed and presented in this paper.
abstractGer	Accuracy improvement is among the primary key research focuses in the area of recommender systems. Traditionally, recommender systems work on two sets of entities, Users and Items, to estimate a single rating that represents a user’s acceptance of an item. This technique was later extended to multi-criteria recommender systems that use an overall rating from multi-criteria ratings to estimate the degree of acceptance by users for items. The primary concern that is still open to the recommender systems community is to find suitable optimization algorithms that can explore the relationships between multiple ratings to compute an overall rating. One of the approaches for doing this is to assume that the overall rating as an aggregation of multiple criteria ratings. Given this assumption, this paper proposed using feed-forward neural networks to predict the overall rating. Five powerful training algorithms have been tested, and the results of their performance are analyzed and presented in this paper.
abstract_unstemmed	Accuracy improvement is among the primary key research focuses in the area of recommender systems. Traditionally, recommender systems work on two sets of entities, Users and Items, to estimate a single rating that represents a user’s acceptance of an item. This technique was later extended to multi-criteria recommender systems that use an overall rating from multi-criteria ratings to estimate the degree of acceptance by users for items. The primary concern that is still open to the recommender systems community is to find suitable optimization algorithms that can explore the relationships between multiple ratings to compute an overall rating. One of the approaches for doing this is to assume that the overall rating as an aggregation of multiple criteria ratings. Given this assumption, this paper proposed using feed-forward neural networks to predict the overall rating. Five powerful training algorithms have been tested, and the results of their performance are analyzed and presented in this paper.
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title_short	Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems
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Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

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