Incorporating Invocation Time in Predicting Web Service QoS via Triadic Factorization

With the development of Service-Oriented technologies, the amount of Web services grows rapidly. QoS-Aware Web service recommendation can help service users to design more efficient service-oriented systems. However, existing methods assume the QoS information for service users are all known and accurate, but in real case, there are always many missing QoS values in history records, which increase the difficulty of the missing QoS value prediction. By considering the user-service-time three dimension context information, we study a Temporal QoS-Aware Web Service Prediction Framework which aims to recommend best candidates to service user's requirements and meanwhile improve the QoS prediction accuracy. One major challenge is that how to deal with the high dimension, sparse QoS value data. Tensor which is known as multi-way array provides a natural representation for such QoS value data. Therefore, we formalize this problem as a tensor factorization model and propose a Tucker Decomposition (TD) algorithm which is able to deal with the triadic relations of user-service-time model. Extensive experiments are conducted based on our real-world QoS dataset collected on Planet-Lab, comprised of service invocation response-time values from 408 users on 5,473 Web services at 56 time periods. Comprehensive empirical studies demonstrate that our approach is more accuracy than other approaches and achieves 100X to 1000X memory space reduction.