Many web-based software systems have been developed in the form of composite services. It is important to accurately predict the Quality of Service (QoS) value of atomic web services because the performance of such composite services depends greatly on the performance of the atomic web service adopted. In recent years, collaborative filtering based methods for predicting the web service QoS values have been proposed. However, they are mainly faced with a cold start problem that is difficult to make reliable prediction due to highly sparse historical data, newly introduced users and web services, and the existing work only deals with the case of newly introduced users. In this article, we propose a Location-based Matrix Factorization using a Preference Propagation method (LMF-PP) to address the cold start problem. LMF-PP fuses invocation and neighborhood similarity, and then the fused similarity is utilized by preference propagation. LMF-PP is compared with existing approaches on the real world dataset. Based on the experimental results, LMF-PP shows better performance than existing approaches in cold start environments as well as in warm start environments.

With the popularity of service-oriented architecture, many web systems have been developed in form of composite services. Since the performance of these composite services highly depends on Quality of Service (QoS) of employed atomic web services, it is important to predict the QoS values of atomic web services with high accuracy. Although collaborative filtering based approaches have recently been proposed to predict the web service QoS values, they mostly face a cold start problem which causes unreliable prediction due to the highly sparse historical data, newly introduced users and web services. Furthermore, existing work only considers the case of newly introduced users. In this paper, we propose a Location-based Matrix Factorization technique via Preference Propagation (LMF-PP) to improve the cold start problem in web service QoS prediction domain. LMF-PP exploits the location information of entities (i.e., Users and web services) and employs the preference propagation to make the accurate QoS prediction even for the newly introduced entities and in the small amount of data (i.e., Highly sparse matrix). The performance of LMF-PP is compared with that of existing approaches on a real world dataset. The experimental results show that LMF-PP can outperform the existing approaches in not only a cold start environment but also a warm start environment.