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

Mehrbakhsh Nilashi, Maryam Salahshour, Othman Ibrahim, Abbas Mardani, Mohammad Dalvi Esfahani, Norhayati Zakuan


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.  


Multi-criteria recommender systems, Accuracy, Fuzzy SOM, Neuro-Fuzzy, Collaborative Filtering

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