Exploiting digital TV users' preferences in a tourism recommender system based on semantic reasoning

Tourism recommender systems match the user preferences against the huge diversity of tourist resources, helping to decide where to go and what to do. Current approaches require the users to initialize their profiles by expressing their interests accurately, which is a very tedious process. We propose a system that automatically infers the users' preferences from their TV viewing histories, i.e., the tourism attractions the users will appreciate are selected by considering the TV contents they watched in the past. To this aim, we have developed a semantics-based filtering strategy that considers both the users' preferences and the interests of like-minded individuals. The resulting recommendations shape a tailor-made travel plan the users can access via domestic and mobile consumer devices.