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A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques

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Abstract

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.

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Acknowledgments

The authors would like to acknowledge the financial support from Fundamental Research Grant Scheme (FRGS) (No. FRGS/1/2014/ICT07/UTM/02/1; vote no R.J130000.7806.4F466) of the Ministry of Education (MOE), Malaysia. The authors also acknowledge the Research Management Centre (RMC) of the Universiti Teknologi Malaysia (UTM) for providing excellent research environment to complete this work. Appreciation also goes to the anonymous reviewers whose comments helped us to improve the manuscript.

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  1. Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

    Mehrbakhsh Nilashi, Othman Bin Ibrahim & Norafida Ithnin

  2. Construction Research Alliance, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

    Rozana Zakaria

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  1. Mehrbakhsh Nilashi
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  2. Othman Bin Ibrahim
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Nilashi, M., Ibrahim, O.B., Ithnin, N. et al. A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques. Soft Comput 19, 3173–3207 (2015). https://doi.org/10.1007/s00500-014-1475-6

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