Personalized Service Technology And System

With the development of the Internet and the extending of applications, resources and applications on the Internet have cross-organizational, technical diversity and heterogeneity features. But traditional rigid, tightly coupled application integration approaches can't survive the opening, changing environment. As one of the emerging patterns for distributed computing, Service-Oriented Computing(SOC) have brought the dawn of distributed applications integration problem in open environments. Though Service-Oriented Architecture(SOA) ensures service resources can be utilized by employing a publish-find-bind pattern, is still difficult for users to understand and use required services directly. Our concern is how to enable the user to get right services among many possible choices.At first, a method of semantic annotation for web service using customizable semantic templates is proposed, which improves the intelligibility of services and provides a basis for the selection and recommendation of services. Secondly, a user behavior model based on bayesian network is proposed, which can reflect users'trend. Thirdly, taking into account the influence of user preference, a user interest model based on vector space model is proposed. Next, Apriori algorithm, as a classical association rule mining algorithm, is improved and applied to personalized service recommendation. Finally, a developed prototype of personalized recommendation system is introduced. Analysis results demonstrated that this personalized service recommendation algorithm has better effect and can meet various user requirements.