A New Method for Collaborative Filtering Recommender Systems: The Case of Yahoo! Movies and TripAdvisor Datasets

Abstract

Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a user’s needs based on data coming from similar users, are becoming increasingly popular as ways to combat information overload. They use a single rating as input. However, the multi-criteria based CF presents a possibility to provide accurate recommendations by considering the user preferences in multiple aspects. This research proposes a new recommendation method using Adaptive Neuro Fuzzy Inference System (ANFIS) and Fuzzy Self-Organizing Map (SOM) for accuracy improvement of multi-criteria recommender systems. We also apply Principal Component Analysis (PCA) for dimensionality reduction and to address multi-collinearity induced from the interdependencies among criteria in multi-criteria CF datasets. Experimental results on Yahoo! Movies and TripAdvisor datasets demonstrated that the proposed method significantly improves recommendation accuracy of multi-criteria CF.