Towards Personalized Ranking in Web Service Selection

Web service discovery is one of the most important problems in implementing Service Oriented Architecture. Several approaches have been proposed to solve this problem among which semantic based web service discovery has been attained much importance by researchers in academic and industry community. However, there is a challenge in semantic web service discovery process, that is, among the retrieved set of semantically equivalent web service candidates, how to discern which one is the best? In this paper, a ranking framework is proposed in which query´s context and web service candidate´s context is explored to assist matching beyond semantic level. We use user´s request history and current request to constitute query´s context while employing association rule to explore web services candidatepsilas context. Furthermore, we propose a RC-IRF method to calculate each candidate´s invocation possibility in query´s context and then the best suitable potential web service can be selected.