Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization

With the rapid development of cloud manufacturing (CMfg), quality-of-service (QoS) prediction becomes increasingly important in CMfg service platform because it turns out to be impractical to acquire all service QoS values. In this paper, we present a neighbourhood enhanced matrix factorization approach to predict missing QoS values. We first systematically consider geographical information, sample set diversity computation and platform context to extend basic Pearson Correlation Coefficient (PCC) similarity and extract neighbourhood information. Then, we integrate neighbourhood information into matrix factorization (MF) and make prediction of missing values. Compared with existing methods, the proposed method has the following new features: (1) entropy information is adopted to derive personal weights for different users or services when computing PCC similarity; (2) location information and sample set similarity are considered to enhance PCC similarity; (3) topology information is introduced to address data sparsity issue; (4) neighbourhood information is incorporated with MF to improve prediction accuracy. We conduct an experiment on a real-world dataset which includes web service invocations from 339 service users on 5825 services to verify the feasibility and efficiency of our method.