# A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems
## 5 Conclusions and Future Work  
Traditional RSs have been recognized as systems that determine user preferences on items using single-rating techniques. In contrast, MCRSs use multiple criteria ratings that cover several item's attributes to predict user preferences for a given user-item pair. A model-based approach is one of the methods that learn the relationships between criteria ratings from an observed data to estimate the unknown ratings by constructing a predictive model. Among such models are those that followed an aggregation function technique, which intuitively assumes a relationship between the overall and criteria ratings. The present study was designed to determine the impact of feedforward neural networks trained with simulated annealing algorithms to build the predictive model for estimating preferences of users on items that are not yet rated by the users. To achieve this, we built four RSs, two of which are single rating RSs using singular value decomposition (\(SVD\)) and slope one algorithm, respectively. The two MCRSs were built using neural networks integrated with \(SVD\) and slope one algorithm called \(MC\)-\(SVD\) and \(MC\)-\(Slope\), respectively. Several experiments were carried out with two different datasets to investigate which of the RSs can provide better prediction accuracy over the other and under which circumstances. The investigations have shown that in all the experiments conducted; the proposed neural network-based approaches produced highest prediction accuracy compared to the single rating techniques. The second significant finding was that the proposed techniques show higher correlations with original data than their corresponding traditional techniques. The evidence from this study suggests that using neural networks in conjunction with single-rating technique--especially matrix factorization (\(SVD\))--is the best way to model MCRSs. Furthermore, the techniques identified in this paper have assisted in our understanding of the role of aggregation function approach and also provide a promising alternative for modeling multiple criteria ratings.  
The neural networks used in this study were three-layer (input layer, one hidden layer, and an output layer) feedforward networks trained using simulated annealing algorithms, which have higher prediction accuracy than quasi-Newton, conjugate gradient, and backpropagation algorithms. Although it minimizes the error efficiently and does not require many iterations, the major weak point of simulated annealing algorithms is the long running time, which could be improved by hybridizing it with faster training algorithms such as a Levenberg-Marquardt algorithm. However, more research on this topic needs to be undertaken to speed up the running time and also to try increasing the size of the network by using techniques like deep belief networks or convolutional neural networks. Exploring other sophisticated machine techniques such as Bayesian reasoning, random forest algorithm, multidimensional scaling, neighborhood search, ant colony optimization, bee colony optimization, and others into MCRSs modeling is also recommended for future research. Moreover, future studies on the current topic that would use datasets from a social network recommendation framework that could incorporate trust as an additional criterion or a hybrid of implicit trust-based ratings that determine the trust based on each criterion and our proposed technique are therefore recommended.  
The authors would like to offer special thanks to the Editors of the _Journal of Applied Sciences_ and the anonymous reviewers for their useful comments and recommendations, which helped significantly in improving the quality of this article.  
[PERSON] conducted the experiments and wrote the first version of the paper. [PERSON] is the research advisor, who proposed the structure of the paper and helped in proofreading and improving the quality of the article.  
The authors declare no conflict of interest.