Recommendation System for Sightseeing Tours

This research proposes a model of a recommendation system (RS) for tourist itineraries. The RS suggests tips of what to visit in a city, based on the available time, personal preferences, current geo-location, and the user's context awareness. These suggestions are calculated based on the treatment of collected data in real time by external application programming interfaces, through a list of points of interest located within a radius that can be reached by the user. Preliminary tests validated the model's goals and its potential in the tourism sector. The RS for tourist itineraries proposed is based on four essential points, in order to make the experience different and well as possible: end-user's personal tastes, the time available, end-user's current location, and context awareness. The performance tests that were carried out brought very positive results and showed that the RS presented a number of requisitions proportional to the server response times and algorithm. The functionality tests were quite positive, with percentages of experience of using the RS between 62.5% and 100%.