Enhancing itinerary recommendation with linked open data

Abstract

This paper proposes a recommender system that exploits linked open data (LOD) to perform a social context-aware cross-domain recommendation of personalized itineraries integrated with multimedia and textual content. To this aim, the recommendation engine considers the user profile, the context of use, and the features of the points of interest (POIs) extracted from LOD sources. We describe how to extract data and process it to perform hybrid filtering. All recommendations are based on the user’s features extracted implicitly and explicitly. These features are used to apply content-based filtering and collaborative filtering, weighing results based on similar users’ experience. The preliminary results of an experimental evaluation on a sample of 20 real users show the effectiveness of the proposed system not only in terms of perceived accuracy, but also in terms of novelty, non-obviousness, and serendipity.