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-...
Ausführliche Beschreibung
Autor*in: |
Mohammed Hassan [verfasserIn] Mohamed Hamada [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2017 |
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In: Computation - MDPI AG, 2014, 5(2017), 3, p 40 |
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Übergeordnetes Werk: |
volume:5 ; year:2017 ; number:3, p 40 |
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DOI / URN: |
10.3390/computation5030040 |
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Katalog-ID: |
DOAJ054172179 |
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10.3390/computation5030040 doi (DE-627)DOAJ054172179 (DE-599)DOAJa2ed21222e4b4f85ad4fd1da27db1623 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Mohammed Hassan verfasserin aut Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. recommender systems artificial neural network genetic algorithm simulated annealing back-propagation Adaline Levenberg-Marquardt Electronic computers. Computer science Mohamed Hamada verfasserin aut In Computation MDPI AG, 2014 5(2017), 3, p 40 (DE-627)751861367 (DE-600)2723192-6 20793197 nnns volume:5 year:2017 number:3, p 40 https://doi.org/10.3390/computation5030040 kostenfrei https://doaj.org/article/a2ed21222e4b4f85ad4fd1da27db1623 kostenfrei https://www.mdpi.com/2079-3197/5/3/40 kostenfrei https://doaj.org/toc/2079-3197 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_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_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 5 2017 3, p 40 |
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10.3390/computation5030040 doi (DE-627)DOAJ054172179 (DE-599)DOAJa2ed21222e4b4f85ad4fd1da27db1623 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Mohammed Hassan verfasserin aut Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. recommender systems artificial neural network genetic algorithm simulated annealing back-propagation Adaline Levenberg-Marquardt Electronic computers. Computer science Mohamed Hamada verfasserin aut In Computation MDPI AG, 2014 5(2017), 3, p 40 (DE-627)751861367 (DE-600)2723192-6 20793197 nnns volume:5 year:2017 number:3, p 40 https://doi.org/10.3390/computation5030040 kostenfrei https://doaj.org/article/a2ed21222e4b4f85ad4fd1da27db1623 kostenfrei https://www.mdpi.com/2079-3197/5/3/40 kostenfrei https://doaj.org/toc/2079-3197 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_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_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 5 2017 3, p 40 |
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10.3390/computation5030040 doi (DE-627)DOAJ054172179 (DE-599)DOAJa2ed21222e4b4f85ad4fd1da27db1623 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Mohammed Hassan verfasserin aut Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. recommender systems artificial neural network genetic algorithm simulated annealing back-propagation Adaline Levenberg-Marquardt Electronic computers. Computer science Mohamed Hamada verfasserin aut In Computation MDPI AG, 2014 5(2017), 3, p 40 (DE-627)751861367 (DE-600)2723192-6 20793197 nnns volume:5 year:2017 number:3, p 40 https://doi.org/10.3390/computation5030040 kostenfrei https://doaj.org/article/a2ed21222e4b4f85ad4fd1da27db1623 kostenfrei https://www.mdpi.com/2079-3197/5/3/40 kostenfrei https://doaj.org/toc/2079-3197 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_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_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 5 2017 3, p 40 |
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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 |
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Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems |
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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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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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|
score |
7.3998404 |