A new web service model of hybrid personalized recommendation
Huichuan Liao · 2013
In recent years, personalized recommendation has become a research focus on the Web services recommendation. The current recommendation system can be improved in the prediction accuracy and recommendation quality. This paper proposes a hybrid personalized recommendation model based on users' behaviors context-aware, which combines content-based filtering with collaborative filtering methods. First, we have selected m service subclasses by using content-based filtering method according to classify characteristics and current user-state. Next, users' specific ratings are predicted by K-nearest neighbor method. Finally, the Top-N services in the subclass will be recommended. Through the final experiment, we can draw a conclusion that the improved algorithm has good recommendation effect and high accuracy.