  In recent years, the number of online services has grown rapidly, invoke the
required services through the cloud platform has become the primary trend. How
to help users choose and recommend high-quality services among huge amounts of
unused services has become a hot issue in research. Among the existing QoS
prediction methods, the collaborative filtering(CF) method can only learn
low-dimensional linear characteristics, and its effect is limited by sparse
data. Although existing deep learning methods could capture high-dimensional
nonlinear features better, most of them only use the single feature of
identity, and the problem of network deepening gradient disappearance is
serious, so the effect of QoS prediction is unsatisfactory. To address these
problems, we propose an advanced probability distribution and location-aware
ResNet approach for QoS Prediction(PLRes). This approach considers the
historical invocations probability distribution and location characteristics of
users and services, and first use the ResNet in QoS prediction to reuses the
features, which alleviates the problems of gradient disappearance and model
degradation. A series of experiments are conducted on a real-world web service
dataset WS-DREAM. The results indicate that PLRes model is effective for QoS
prediction and at the density of 5%-30%, which means the data is sparse, it
significantly outperforms a state-of-the-art approach LDCF by 12.35%-15.37% in
terms of MAE.
