Personalized Location-Aware QoS Prediction for Web Services Using Probabilistic Matrix Factorization

QoS prediction is critical to Web service selection and recommendation, with the extensive adoption of Web services. But as one of the important factors influencing QoS values, the geographical information of users has been ignored before by most works. In this paper, we first explicate how Probabilistic Matrix Factorization (PMF) model can be employed to learn the predicted QoS values. Then, by identifying user neighbors on the basis of geographical location, we take the effect of neighbors’ experience of Web service invocation into consideration. Specifically, we propose two models based on PMF, i.e. L-PMF and WL-PMF, which integrate the feature vectors of neighbors into the learning process of latent user feature vectors. Finally, extensive experiments conducted in the real-world dataset demonstrate that our models outperform other well-known approaches consistently.