Recommending Web Services via Combining Collaborative Filtering with Content-Based Features

With increasing adoption and presence of Web services, designing novel approaches for efficient Web services recommendation has become steadily more important. Existing Web services discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant Web service search engines, which possess many limitations such as insufficient recommendation performance and heavy dependence on the input from users such as preparing complicated queries. In this paper, we propose a novel approach that dynamically recommends Web services that fit users' interests. Our approach is a hybrid one in the sense that it combines collaborative filtering and content-based recommendation. In particular, our approach considers simultaneously both rating data and content data of Web services using a three-way aspect model. Unobservable user preferences are represented by introducing a set of latent variables, which is statistically estimated. To verify the proposed approach, we conduct experiments using 3, 693 real-world Web services. The experimental results show that our approach outperforms the two conventional methods on recommendation performance.