Towards a M-Tourism System for the Recommendation Of the Points of Interest

In recent years, there has been an impressive evolution of mobile devices (Tablet, smartphone ...) and
location technologies (GPS, GPRS, 3G, 4G ...). This evolution has given rise to applications in several areas
such as commerce, tourism ... because smartphones allow users to assist their apprenticeship anywhere,
anytime. For example, tourists are often confronted with situations during their travels where they have to make decisions and make choices (what to do, which place to visit, what to eat ...). These choices are
determined by their preferences And especially by their locations.
Faced with these situations, smartphones can allow tourists to access to their travel documents (passport, airline ticket, ...), to their money in various forms (credit card, electronic wallet, ...) and especially instant feedback on POIs (point of interest) to discover. However, this generates a large volume of POIs and requires instant and personalized learning.
For this reason, recommendation systems can assist users during their visits by providing POIs adapted to their contexts of discovery of a new environmenThese systems facilitate the decision-making process significantly improve and personalize the itinerary of a person by taking account the interests of the user and the constraints of travel. Our work makes it possible to instantly recommend places to visit by tourists during their travels. This m-tourism system uses usage
statistics and visitor appreciation comments to better refine the choice of POIs.
To achieve this objective, we begin by explaining the learning characteristics in a context of m-tourism.
Then, let us approach the different techniques of recommendations used with a particular interest for the
algorithms of collaborative filtering. Finally, we present a conceptual view of our system and the architecture related to our prototype for POI recommendation.