Quality of Web Service Prediction by Collective Matrix Factorization

This paper studies the quality of web service prediction problem. We formalize the QoS prediction problem by incorporating multiple contextual characteristics via collective matrix factorization that simultaneously factor the user-service quality matrix and contextual information matrices. Using the service category and location context, we develop three context-aware QoS prediction models and algorithms to demonstrate the advantages of this modeling technique. The advantages of our proposed models are demonstrated via experiments on real-life data sets.