Clustering-based uncertain QoS prediction of web services via collaborative filtering

Although collaborative filtering (CF) has been widely applied for QoS-aware web service recommendation, most of these approaches mainly focus on certain QoS prediction. However, they failed to take the natural characteristic of web services with QoS uncertainty into account in service-oriented web applications. To solve the problem, this paper proposes a novel approach for uncertain QoS prediction via collaborative filtering and service clustering. We first establish uncertain QoS model for a service user, where each service is formalised as a QoS matrix. To mine the similar neighbourhood users for an active user, we then extend the Euclidean distance to calculate the similarity between two uncertain QoS models. Finally, we present two kinds of QoS prediction strategies based on collaborative filtering and clustering, called U-Rec and UC-Rec. Extensive experiments have been carried on 1.5 million real-world uncertain QoS transaction logs of web services. The experimental results validate the effectiveness of our proposed approach.