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 Hence, using outdated
QoS
values 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model to filter out outdated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
QoS
values 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.