Evaluating the performance of a neural network-based multi-criteria recommender system

Abstract

Frequent use of internet applications and rapid increase in volumes of resources have made it difficult for online users to effectively make decisions on the kinds of information or items to select. Recommender systems (RSs) are intelligent decision-support tools that exploit preferences of users and suggest items that might be interesting to them. RSs are one of the various solutions proposed to address the problems of information overload. Traditionally, RSs use single rating techniques to predict and represent preferences of users for items that are not yet seen. Multi-criteria RSs use multiple ratings to various attributes of items for improving prediction and recommendation accuracy of the systems. However, one major challenge of multi-criteria RSs is the choice of an efficient approach for modelling the criteria ratings. Therefore, this paper aimed at employing artificial neural networks to model the criteria ratings and determine the predictive performance of the systems based on aggregation function approach. Seven evaluation metrics have been used to evaluate and measure the accuracy of the systems. The empirical results of the study have shown that the proposed technique has the highest prediction and recommendation than the corresponding traditional technique.Reference to this paper should be made as follows: Hassan, M. and Hamada, M. (2019) 'Evaluating the performance of a neural network-based multi-criteria recommender system', Int.Evaluating the performance of a neural network-based multi-criteria 55 Mohamed Hamada is currently a Senior Associate Professor working in the Software Engineering Lab, at the University of Aizu, Aizuwakamatsu, Fukushima, Japan. He obtained his PhD from the University of Tsukuba, Japan. His research interest is in artificial intelligence and the study of functional logic languages, natural inspired computation, cloud computing-based e-learning and m-learning systems, multimedia, and web-based and advanced learning technologies. He is also interested in smart devices (such as smartphones and tablets) applications development and innovation, and so on. He is a regular Visiting Professor at Fatih University, Istanbul, Turkey and the African This paper is a revised and expanded version of a paper entitled 'Performance analysis of neural networks-based multi-criteria recommender systems' presented at

Towards QoS-Aware Recommendations

“…Similar approaches could be considered for the design of QoS-aware RSs. Our work provides initial insights, which can be helpful in the tuning of the pre/post-filtering algorithms, or the development of models amenable to multi-criteria optimization [18,19,24,32,36].…”

Analysis of Perceived Usefulness and Perceived Ease of Use in Relation to Employee Performance

“…Employee satisfaction is indeed increasingly difficult for businesses, especially a few in the smartphone industry. The key concern in employee happiness would regard its individuals as securities and the important determinant and doorway to performance and massive profits (Amara & Subramanian, 2020; Anaam, Magableh, et al, 2021;Hassan & Hamada, 2019;Liang, 2023;Zakuan et al, 2018). In conclusion, the most recent research investigated the impact of employee job satisfaction as a dependent factor, in addition to organizational characteristics (Jangizehi et al, 2023;Parapar et al, 2013;Shambour et al, 2016).…”