Smoothing approach to alleviate the meager rating problem in collaborative recommender systems

Abstract

In this paper, an integrated recommendation approach using Radial Basis Function Network (RBFN) and Collaborative Filtering (CF) is proposed. Radial basis function network is a neural network approximation method used to improve the accuracy of the recommendations. The proposed system RBFN_KFCM has offline and online phases. During offline, the system uses RBFN for smoothing and kernel fuzzy c-means (KFCM) method for clustering. During online recommendation, KFCM based approach is proposed. At the end of each session the clusters are updated by replacing the smoothed rating by the original rating. A comparison is made with benchmark recommender systems such as item-based, user-based systems, singular value decomposition (SVD) and also popular machine learning techniques such as Support Vector Machine (SVM), Multilayer Perceptron (MLP) using backpropagation algorithm, in terms of accuracy, decision-support measures and computational time. Empirical evaluation is carried out by using movielens dataset.