QoS-Aware Web Service Recommendation using Collaborative Filtering with PGraph

Web service recommendation plays an important role in building reliable service-oriented systems for both the service providers and the active users. However, with the proliferation of web services on the World Wide Web, traditional service recommendation is hard to accurately provide customized services to active users. In this paper, we propose a novel web service recommender model using collaborative filtering to improve the prediction of Quality-of-Services. Benefiting from the accuracy of hybrid recommenders, we extend the idea of optimized predicting order and design the PGraph to describe the neighborhood. Furthermore, a new algorithm using adjusted topological sorting for PGraph is proposed to generate the optimized order while predicting. Finally, we conduct extensive experiments to evaluate our proposed model, in which a real data set with 1.5 million invocation information is taken as input. The experiment results show that our model achieves higher prediction accuracy than other models.