A user dependent Web service QoS collaborative prediction approach using neighborhood regularized matrix factorization

With the advent of service-oriented computing, Web service QoS prediction have become crucial for the success of many service-based distributed systems due to its significant role in evaluating Web services to satisfy user personalized needs. Previous studies underestimate the role of implicit relationship of neighborhood with respect to both users and Web services. It is natural and reasonable to assume that the QoS Web services perform and the choices users make are significantly influenced by their neighbors, thus it will be beneficial to incorporate this new source of information in QoS prediction. In this paper, we propose a neighborhood regularized matrix factorization method for collaborative Web service QoS prediction by properly incorporating both user and Web service neighborhood relationships aiming to improve the accuracy of prediction as well as alleviate the data sparsity issue. Experimental results demonstrate that the proposed approach achieves more accurate prediction than other well-known methods.