QoS Evaluation for Web Service Recommendation

Web service recommendation is one of the most important fields of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown QoS property values and the evaluation of overall QoS according to user preferences. Aiming to address these two problems and their current challenges, we propose two efficient approaches to solve these problems. First, unknown QoS property values were predicted by modeling the high-dimensional QoS data as tensors, by utilizing an important tensor operation, i.e., tensor composition, to predict these QoS values. Our method, which considers all QoS dimensions integrally and uniformly, allows us to predict multi-dimensional QoS values accurately and easily. Second, the overall QoS was evaluated by proposing an efficient user preference learning method, which learns user preferences based on users' ratings history data, allowing us to obtain user preferences quantifiably and accurately. By solving these two core problems, it became possible to compute a realistic value for the overall QoS. The experimental results showed our proposed methods to be more efficient than existing methods.