Personalized Services Recommendation Based on Context-Aware QoS Prediction

Abstract

With the increase of published Web services, it has become a great challenge to recommend service consumers the best services with regard to the quality of services (QoS). Collaborative filtering is often employed to predict the QoS of a specific service to a certain consumer. However, in existing collaborative filtering based service recommendation approaches, the context under which consumers submit a recommendation request is seldom taken into account when filtering similar recommenders and their corresponding experience. In this paper, we propose a new method dubbed CASR (Context-Aware Services Recommendation) by referring to previous service invocation experiences under similar context with the current consumer, which is of great importance in the personalized service recommendation system. First, the proposed algorithm clusters the service invocation records according to the similarity on context properties and selects the cluster that is most similar to the context of current consumer. Then it predicts the QoS of an unused service for current consumer based on the filtered recommendation records by Bayesian inference. Experimental results demonstrate that the proposed approach can significantly improve the accuracy of QoS prediction and service recommendation.