FRAIPA: A fast recommendation approach with improved prediction accuracy

Recommender systems have emerged as a key tool to overcome the negative impact of information overload problem, as well as, help the users seek the relevant information based on their past preferences. Collaborative filtering represents a widely used approach to build recommendation systems. In essence, many methods have been developed to provide high quality results, neverthless, they may incur prohibitive computational costs. In this paper, a novel method called FRAIPA is proposed, which is designed to tackle the sparsity, dynamic data problems, moreover, it improves the prediction accuracy and computation time. Experimental results on two real-world data sets reveal the effectiveness of the proposed method over existing methods.