A Highly Accurate Prediction Algorithm for Unknown Web Service QoS Values

Abstract

Quality of Service (QoS) guarantee is an important component of service recommendation. Generally, some QoS values of a service are unknown to its users who has never invoked it before, and therefore the accurate prediction of unknown QoS values is significant for the successful deployment of Web service-based applications. Collaborative filtering is an important method for predicting missing values, and has thus been widely adopted in the prediction of unknown QoS values. However, collaborative filtering originated from the processing of subjective data, such as movie scores. The QoS data of Web services are usually objective, meaning that existing collaborative filtering-based approaches are not always applicable for unknown QoS values. Based on real world Web service QoS data and a number of experiments, in this paper, we determine some important characteristics of objective QoS datasets that have never been found before. We propose a prediction algorithm to realize these characteristics, allowing the unknown QoS values to be predicted accurately. Experimental results show that the proposed algorithm predicts unknown Web service QoS values more accurately than other existing approaches.

QoS prediction for Web service in Mobile Internet environment

“…To verify the performance of our approach, we implement our approach 3 and conduct experiments based our previous work (Ma et al, 2015;Wang et al, 2014a, Wang et al, 2011aWang et al, 2014b;Wang, Zhu, & Yang, 2014c;Wang, Sun, & Yang, 2010) using a dataset 4 named WSDream. It contains nearly one million service response time records.…”

Personalized and Accurate QoS Prediction Approach Based on Online Learning Matrix Factorization for Web Services

“…Zheng et al [8] proposed a hybrid collaborative filtering algorithm which combines UPCC and IPCC. Ma et al [9] presented a highly accurate prediction algorithm (HAPA) to predict unknown QoS values by keeping the original linear relationship. However, neighborhood-based CF method predicts QoS by employing the values of similar users or similar items.…”

Exploring the Evolution of New Mobile Services

“…Sun et al [16] studied the mobile usage prediction problem using feature comparison analysis. To realize a dynamic home screen application, Shin et al [17] built a prediction model [18,19] for the usage of apps in mobile context. Zhu et al [20] studied service usage patterns from the perspective of users and proposed an application usage prediction method.…”