Context-Aware Ubiquitous Web Services Recommendation Based on User Location Update

摘要: In this paper, we propose a novel ubiquitous Web service recommendation approach to context-aware based on user location update (CASR-ULU). First, model the influence of preference expansion. Second, perform similarity mining for updated location. Third, predict Quality Service by Bayesian inference, and thus recommend ideal specific subsequently. Furthermore, calendar Android mobile application is implemented testify CASR-ULU algorithm in environment. Finally, evaluate method WS-Dream dataset with evaluation matrices such as RMSE MAE. Experimental results show that our achieves competitive performance environment, compared several state-of-the-art methods.