Anatomy of a Semantic Web-enabled Knowledge-based Recommender System

Knowledge-based Recommender Systems suggest to users items of their interest, on the basis of some understanding of both items’ characteristics and users’ profiles. In order to properly work, this kind of recommender systems need a thorough modeling of items and users; the usual barrier to their development is, therefore, the availability of the necessary knowledge and its maintenance over time. With this respect, Semantic Web technologies can be of great help: not only knowledge technologies and languages can be employed to build the knowledge base, but the large availability of open and linked data about a growing variety of fields and topics, published on the Web of Data, further simplifies the modeling step for recommender systems. In this paper, we present our concept of Semantic Web-enabled Recommender System, based on the retrieval from the linked data Web of the necessary pieces of knowledge about items and users. We illustrate the general structure of this new family of Knowledge-based Recommender Systems and we explain how we concretely followed this approach to develop a tool to recommend Web services in the context of the SOA4All project. We also offer our considerations about the strengths, the current limitations and the possible extensions of our proposal.