caa a22 4500 60547091X CHVBK 20210128100330.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00500-014-1475-6 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1475-6 A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques [Elektronische Daten] [Mehrbakhsh Nilashi, Othman Ibrahim, Norafida Ithnin, Rozana Zakaria] Multi-criteria collaborative filtering (MC-CF) presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects of items. However, scalability and sparsity are two main problems in MC-CF which this paper attempts to solve them using dimensionality reduction and Neuro-Fuzzy techniques. Considering the user behavior about items' features which is frequently vague, imprecise and subjective, we solve the sparsity problem using Neuro-Fuzzy technique. For the scalability problem, higher order singular value decomposition along with supervised learning (classification) methods is used. Thus, the objective of this paper is to propose a new recommendation model to improve the recommendation quality and predictive accuracy of MC-CF and solve the scalability and alleviate the sparsity problems in the MC-CF. The experimental results of applying these approaches on Yahoo!Movies and TripAdvisor datasets with several comparisons are presented to show the enhancement of MC-CF recommendation quality and predictive accuracy. The experimental results demonstrate that SVM dominates the K-NN and FBNN in improving the MC-CF predictive accuracy evaluated by most broadly popular measurement metrics, F1 and mean absolute error. In addition, the experimental results also demonstrate that the combination of Neuro-Fuzzy and dimensionality reduction techniques remarkably improves the recommendation quality and predictive accuracy of MC-CF in relation to the previous recommendation techniques based on multi-criteria ratings. Springer-Verlag Berlin Heidelberg, 2014 Adaptive Neuro-Fuzzy inference systems nationallicence Higher order singular value decomposition nationallicence Multi-criteria collaborative filtering nationallicence Predictive accuracy nationallicence Classification nationallicence Nilashi Mehrbakhsh Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia aut Ibrahim Othman Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia aut Ithnin Norafida Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia aut Zakaria Rozana Construction Research Alliance, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia aut Soft Computing Springer Berlin Heidelberg 19/11(2015-11-01), 3173-3207 1432-7643 19:11<3173 2015 19 500 https://doi.org/10.1007/s00500-014-1475-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1475-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Nilashi Mehrbakhsh Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia aut NATIONALLICENCE 700 1- Ibrahim Othman Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia aut NATIONALLICENCE 700 1- Ithnin Norafida Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia aut NATIONALLICENCE 700 1- Zakaria Rozana Construction Research Alliance, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/11(2015-11-01), 3173-3207 1432-7643 19:11<3173 2015 19 500