A Web Services Selection Approach Based on Personalized QoS Prediction

QoS (quality of service) prediction of web services plays an important role in selecting services when a consumer wants to try the services which he never used. Considering different consumers have different characteristics and different QoS experiences, we proposed a personalized QoS predict approach based on collaborative filtering to improve the effectiveness of predicting in this paper. The basic idea of our approach is to find out similarity between the consumers with collected QoS data and then to make prediction for the unused services based on the similarity. Experimental results show that our approach can significantly improve the effectiveness of predicting web services QoS. When there are not sufficient QoS data available for similarity minding, our approach still present good potential to give better prediction of Web services´ QoS.