A new model for trustworthy web service QoS prediction

The number of web services available on the internet has exploded, and as a result, the number of services with the same functionality has exploded as well. Therefore, selecting the best web service from functionally similar services is a critical task in the web service domain. The Quality of Service (QoS) is one of the most common criteria used to select the best web service. Collaborative filtering (CF) has been utilized in several studies to predict the values of QoS attributes of web services for each user in a personalized way. The QoS histories of other users are employed in these methods to predict the QoS values of the active user. Although these methods function well and produce acceptable prediction results, the accuracy of their predictions can be harmed by incorrect data provided by untrustworthy users. In this study, we propose a new model that reduces the impact of unreliable user data, resulting in a trustworthy prediction. This model can be applied to any existing prediction method. In experiments, the proposed model was applied to seven known prediction methods. The results indicate that this model is able to eliminate the impact of unreliable users.

Automated summary

With the rapid increase in the number of web services, selecting the best one has become crucial, with Quality of Service being a common criterion. A new model has been proposed in this study to reduce the impact of unreliable user data and produce trustworthy predictions. Experiments showed that this model can eliminate the influence of unreliable users when applied to existing prediction methods.