Towards Learning Personalized Semantic Relevance Paths in Dialogue Systems

When interacting with information access systems, users have typically distinct interests, preferences and goals that may significantly influence the way they judge the relevance of these systems' outputs. User modeling aims at capturing such interests and preferences in the form of personalized user profiles which could be then used by the systems to provide tailored information services to their users. In this context, we present in this paper a novel ontology-based user profile for personalized information access which, different from the majority of existing approaches, captures user preferences in the form of semantic relevance paths within the ontology graph. More importantly, we present an automated approach for learning and maintaining such profiles in dialogue systems by taking advantage of the dialogue-based interaction between the system and the user and eliciting from the latter important feedback regarding the relevance of the provided information.