Web Service Personalized Quality of Service Prediction via Reputation-Based Matrix Factorization


With the quick development of Net services in service-oriented systems, the necessity of economical Quality of Service (QoS) analysis methods becomes strong. However, many QoS values are unknown truly. Thus, it's necessary to predict the unknown QoS values of Web services primarily based on the obtainable QoS values. Generally, the QoS values of comparable users are employed to create predictions for the present user. But, the QoS values may be contributed from unreliable users, leading to inaccuracy of the prediction results. To handle this downside, we gift a highly credible approach, referred to as reputation-primarily based Matrix Factorization (RMF), for predicting the unknown Net service QoS values. RMF first calculates the name of each user based on their contributed QoS values to quantify the credibility of users, and then takes the users' name into thought for achieving more correct QoS prediction. Name-primarily based matrix factorization is applicable to the prediction of QoS data within the presence of unreliable user-provided QoS values. In depth experiments are conducted with real-world Internet service QoS information sets, and the experimental results show that our proposed approach outperforms alternative existing approaches.

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