Multi-Layered Ontology-Based User Profiles and Semantic Social Networks for Recommender Systems Iván Cantador and Pablo Castells Escuela Politécnica Superior, Universidad Autónoma de Madrid Campus de Cantoblanco, 28049 Madrid, Spain {ivan.cantador, pablo.castells}@uam.es Abstract. This paper describes a strategy that automatically clusters ontology- based user profiles taking into account their common interests for domain con- cepts. The obtained semantic clusters are used to identify similarities among indi- viduals at multiple semantic preference layers, and to define emergent, layered so- cial networks that can be applied in collaborative and recommender systems. As an applicative development of our method, we have experimented with building a personalized information retrieval model that provides ranked item lists based on the existing concept clusters and multi-layered user networks. 1 Introduction The rapid development, spread, and convergence of information and communication technologies are leading to new ways of inter-personal connection, communication, and collaboration. Virtual communities and computer-supported social networks [5,6] are starting to proliferate in increasingly sophisticated ways, opening new research opportu- nities on social group analysis, modeling, and exploitation. Finding hidden links be- tween users based on the similarity of their preferences or historic behavior is not a new idea. In fact, this is the essence of the well-known collaborative recommender systems (e.g. see the survey given in [7]). However, in typical approaches, the comparison be- tween users is done globally, in such a way that partial, but strong and useful similarities may be missed. For instance, two people may have a highly coincident taste in cinema, but a very divergent one in sports. The opinions of these people on movies could be highly valuable for each other, but risk to be ignored by many collaborative recom- mender systems, because global similarity between the users is low. In this paper we propose a multi-layered approach to social networking. Like in pre- vious approaches, our method builds and compares profiles of user interests for semantic topics and specific concepts, in order to find similarities among users. But in contrast to prior work, we divide the user profiles into clusters of cohesive interests, and based on this, several layers of social networks are found. This provides a richer model of inter- personal links, which better represents the way people find common interests in real life. Our approach is based on an ontological representation of the domain of discourse where user interests are defined. The ontological space takes the shape of a semantic network of interrelated domain concepts. Taking advantage of the relations between concepts, and the (weighted) preferences of users for the concepts, our system clusters