Exploiting Cloud Technologies and Context Information for Recommending Touristic Paths

In the developing of applications for touristic paths planning, context-aware recommendation services are gaining more and more relevance. Recommender applications can accommodate location's dependent information with user's needs in a mobile environment, related to the touristic domain. In this paper, we propose a touristic context-aware recommendation system based on both the experience of previous users and on personal preferences of tourists. The information are gathered by several heterogeneous sources (sensors, web portals, repositories related to touristic events and locations) and are stored and analyzed in a cloud architecture that is particularly suitable to process and manage the huge amount of data extracted.