User-QoS-Based Web Service Clustering for QoS Prediction

Abstract

QoS prediction has become an important step in service recommending and selecting. Most QoS prediction approaches are using collaborative filtering as a prediction technique. But collaborative filtering may suffer from data sparsity problem which degrade the prediction accuracy. In order to alleviate the data sparsity problem of collaborative filtering, we presented a hybrid QoS prediction approach by applying clustering on web services before applying collaborative filtering (named services clustering QoS prediction, SCQP). The clustering process cluster web services in to service clusters in which services have the same physical environment. Then the similarity between users is calculated based on these service clusters instead of individual services. So that there are more information to be used when calculate the similarity and it will contribute to elevate the prediction precision. The experimental results showed that our hybrid approach could not only achieve higher prediction precision, but also reduce the computation time than other collaborative filtering based prediction methods.