Time-Aware Semantic Web Service Recommendation

New Web services are emerging on the Internet, while some other Web services are obsolete for some reasons. Unfortunately, not all services are developed in accordance with rules of loose coupling in the software engineering. The consequence is that some services work well only with other services of older versions. The situation is much worse when we deal with composite services. Moreover, using current technology to discover proper semantic services for a composite service is time-consuming and inaccurate. To deal with these problems, we proposed a Web service similarities measurement method and a recommendation method. Based on ontology and information retrieval techniques, we compute among Web services. Then the similarities are used to classify services according to their topics, functionality and semantics. Our recommendation method is able to recommend proper component services to the composite service according to the history information of invocations and similar composite services. The experiments show that our clustering method, which is based on matrix decomposition and Ontology technologies, is more accurate than others, and our recommendation method has less average error than others in the series of missing rate.