Collaborative Itinerary Recommender Systems
Liviu‐Adrian Cotfas · 2011
Although several itinerary recommender systems have been proposed in the literature, they usually don’t allow users to further improve the available set of tourist attractions by submitting information in a collaborative manner. In this paper we present a collaborative itinerary recommender system that relies on crowd sourcing to both expand and maintain current the dataset of attractions. The system combines advanced recommendations and hybrid multiobjective genetic algorithms in order to build personalized itineraries. While the recommendation algorithm allows users to easily find interesting tourist attractions, the multi-objective genetic algorithm offers the possibility of choosing between several Pareto-optimal itinerary solutions. A crowd sourcing approach is used in order to better predict the transport and visiting times. The user interface relies on the latest web technologies in order to support a wide array of mobile devices and to provide a rich user experience.