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Computation 2017, 5(3), 40; doi:10.3390/computation5030040

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

1,2,†,* and 1,†
1
Software Engineering Lab, Graduate School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Japan
2
Department of Software Engineering, Bayero University Kano, Kano 700231, Nigera
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 6 August 2017 / Revised: 4 September 2017 / Accepted: 12 September 2017 / Published: 13 September 2017
(This article belongs to the Section Computational Engineering)
View Full-Text   |   Download PDF [584 KB, uploaded 13 September 2017]   |  

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. View Full-Text
Keywords: recommender systems; artificial neural network; genetic algorithm; simulated annealing; back-propagation; Adaline; Levenberg-Marquardt recommender systems; artificial neural network; genetic algorithm; simulated annealing; back-propagation; Adaline; Levenberg-Marquardt
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hassan, M.; Hamada, M. Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems. Computation 2017, 5, 40.

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