A POV-based user model: From learning preferences to learning personal ontologies

In recent years a variety of ontology-based recommender systems, which make use of a domain ontology to characterize the user model, have shown to be very effective. There are however some open issues with this approach, such as: 1) the creation of an ontology is an expensive process; 2) the ontology seldom takes into account the perspectives of target user communities; 3) different groups of users may have different domain conceptualizations; 4) the ontology is usually static and not able to learn automatically new semantic relationships or properties. To address these points, I propose an approach to automatically build multiple personal ontology views (POVs) from user feedbacks, tailored to specific user groups and exploited for recommendation purpose via spreading activation techniques. © 2013 Springer-Verlag.