Time-Aware Collaborative Filtering for QoS-Based Service Recommendation

Chengyuan Yu, Linpeng Huang

Abstract: In QoS-based Web service recommendation, predicting QoS(Quality of Service) for service users will greatly aid service selection and discovery. In order to improve the prediction accuracy of Collaborative filtering algorithms, various factors are taken into account (e.g., location factor, environment, etc.). But seldom do investigators take the factor of time into account. Actually, QoS performance of Web services is highly related to the service status and network environments which are variable against time. Thus, this paper proposes a time-aware collaborative filtering algorithm to predict the missing QoS values. To validate our algorithm, this paper conducts series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that the time-aware collaborative filtering algorithm significantly improves prediction accuracy.