Journal Article

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

Ithnin, Norafida, Zakaria, Rozana, Nilashi, Mehrbakhsh, Ibrahim, Othman Bin

2015

Overview

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.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Algorithms / Artificial Intelligence / Artificial neural networks / Collaboration / Computational Intelligence / Control / Engineering / Fuzzy logic / Machine learning / Mathematical Logic and Foundations / Mechatronics / Methodologies and Application / Multiple criterion / Ratings & rankings / Recommender systems / Reduction / Robotics / Singular value decomposition / Supervised learning / User profiles