期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：4
DOI：10.14569/IJACSA.2020.01104103
出版社：Science and Information Society (SAI)
摘要：Recent developments in the field of recommender systems have led to a renewed interest in employing some of the sophisticated machine learning algorithms to combine multiple characteristics of items during the process of making recom-mendations. Considerable number of research papers have been published on multi-criteria recommendation techniques. Most of these studies have focused only on using some basic statistical methods or simply by extending the similarity computation of the traditional heuristic-based techniques to model the system. Researchers have not treated the uncertainty that exists about the relationship between multi-criteria modelling approaches and effectiveness of some of the complex and powerful machine learning techniques; in fact, no previous study has investigated the role of artificial neural networks to design and develop the system using aggregation function approach. This paper seeks to remedy these challenges by analysing the performance of multi-criteria recommender systems, modelled by integrating an adaptive linear neuron that was trained using delta rule, and asymmetric sin-gular value decomposition algorithms. The proposed model was implemented, trained and tested using a multi-criteria dataset for recommending movies to users based on action, story, direction, and visual effects of movies. Taken together, the empirical results of the study suggested that there is a strong association between artificial neural networks and the modelling approaches of multi-criteria recommendation technique.
关键词：Multi-criteria recommender systems; adaptive linear neuron; artificial neural network; singular value decomposition; prediction accuracy
