Performance Comparison of Featured Neural Network Trained with Backpropagation and Delta Rule Techniques for Movie Rating Prediction in Multi-criteria Recommender Systems

Abstract

Recommender systems are software tools that have been widely used to recommend valuable items to users. They have the capacity to support and enhance the quality of decisions people make when nding and selecting items online. Such systems work based on which techniques are used to estimate users' preferences on potentially new items that might useful to them. Traditionally, the most common techniques used by many existing recommendation systems are collaborative ltering, content-based, knowledge-based and hybrid-based which combines two or more techniques in different ways. The multi-criteria recommendation technique is a new technique used to recommend items to users based on ratings given to multiple attributes of items. This technique has been used and proven by researchers in industries and academic institutions to provide more accurate predictions than traditional techniques. What is still not yet clear is the role of some machine learning algorithms such as the articial neural network to improve its prediction accuracy. This paper proposed using a feedforward neural network to model user preferences in multi-criteria recommender systems. The operational results of experiments for training and testing the network using two training algorithms and Yahoo!Movie dataset are also presented.