Combining Collaborative Filtering and Semantic Content-Based Approaches to Recommend Web Services

As the abundance of web services on the World Wide Web increase, designing effective approaches for web service selection and recommendation has become more and more important. In this paper we focus on an approach dynamically offering services that fit the end-users´ interests. To this end, we present a hybrid approach, coupling pure and classic collaborative-filtering methods and a semantic content-based method. On the one hand the former methods are used to automatically recommend services depending on other similar users, based on profiles, preferences and historical experience. On the other hand our semantic content-based approach performs Description Logic based reasoning on semantic descriptions of services, in order to analysis semantic similarity of services. This approach further restricts the potential results and then ensuring a semantic recommendation of services. Finally we discuss its advantages and weaknesses.