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Appl. Sci. 2017, 7(9), 868; doi:10.3390/app7090868

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

Software Engineering Lab, Graduate School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu-city 965-8580, Japan
These authors contributed equally to this work.
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Received: 28 July 2017 / Revised: 15 August 2017 / Accepted: 16 August 2017 / Published: 25 August 2017
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

Accuracy improvement has been one of the most outstanding issues in the recommender systems research community. Recently, multi-criteria recommender systems that use multiple criteria ratings to estimate overall rating have been receiving considerable attention within the recommender systems research domain. This paper proposes a neural network model for improving the prediction accuracy of multi-criteria recommender systems. The neural network was trained using simulated annealing algorithms and integrated with two samples of single-rating recommender systems. The paper presents the experimental results for each of the two single-rating techniques together with their corresponding neural network-based models. To analyze the performance of the approach, we carried out a comparative analysis of the performance of each single rating-based technique and the proposed multi-criteria model. The experimental findings revealed that the proposed models have by far outperformed the existing techniques. View Full-Text
Keywords: recommender systems; artificial neural network; simulated annealing; slope one algorithm; singular value decomposition recommender systems; artificial neural network; simulated annealing; slope one algorithm; singular value decomposition
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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. A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems. Appl. Sci. 2017, 7, 868.

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