Context-Aware Web Services Recommendation Based on User Preference Expansion

Context-Aware Recommender System is known to not only recommend items or services similar to those already rated with the highest score, but also consider the current contexts for personalized Web services recommendation. Specifically, a key step for CARS methods refers to previous service invocation experiences under the similar context of the user to make Quality of Services prediction. Existing works either considered the influence of regional correlations on user preference, or combined the location-aware context with the matrix factorization method. However, the user preference expansion triggered by instant update of user location is not fully observed. For instance, when making Web service recommendation for a user, it is expected to be aware of rapid change of the user location immediately and the expansion of user preference as well. In this paper, we propose a Web services recommendation approach dubbed as CASR-UPE (Context-aware Web Services Recommendation based on User Preference Expansion). First, we model the influence of user location update on user preference. Second, we perform the context-aware similarity mining for updated location. Third, we predict the Quality of Services by Bayesian inference, and thus recommend the best Web service for the user subsequently. Finally, we evaluate the CASR-UPE method on WS-Dream dataset by evaluation matrices such as RMSE and MAE. Experimental results show that our approach outperforms several benchmark methods with a significant margin.