A Missing QoS Prediction Approach via Time-Aware Collaborative Filtering

Quality of Service (QoS) guarantee is an important issue in building service-oriented applications. Generally, some QoS values of a service are unknown to its users who have never invoked the service before. Fortunately, collaborative filtering (CF)-based methods are proved feasible for missing QoS prediction and have been widely used. However, these methods seldom took the temporal factors into consideration. Indeed, historical QoS values contain more information about user (or service) similarity. Furthermore, as the application environment is dynamic, obtained QoS values usually have short timeliness. Hence, using outdated QoSvalues will largely decrease the prediction accuracy. In order to resolve this issue, we proposed a time-aware collaborative filtering approach. First, we proposed a QoS model to filter out outdated QoS values, and divided the obtained QoSvalues into several time slices. Then, we computed the average value of historical QoS as temporal QoS forecast. In addition, by introducing time-aware similarity computation mechanism, we succeeded to select real similar neighbor users (or services) and further predict the CF-based QoS based on CF technology. Finally, we can predict the final missing QoS by combining temporal QoS forecast and CF-based QoS prediction. Experiment results show that our approach can receive better prediction precision.

Automated summary

QoS guarantee is crucial in building service-oriented applications. Previous CF methods neglected temporal factors and resulted in decreased prediction accuracy due to outdated QoS values. To address this, our time-aware collaborative filtering approach combines historical QoS and CF technology to predict missing QoS with improved accuracy.