A social navigation guide using augmented reality
Félix Mata, Christophe Claramunt · 2014
Social networks provide rich data sources for analyzing people activities. This paper introduces a mobile recommender system that suggests places to visit to tourists acting in the city of Mexico. The system developed generates itineraries based on the implicit users' behaviors. Recommendations are automatically extracted and analyzed from Twitter thanks to the application of Bayes and Tree algorithms. Suggested itineraries are cross-analyzed to take into account user profiles and preferences. The recommender system provides an augmented reality navigation system that suggests itineraries to the users according to some places of interest. The preliminary prototype developed is an Android app so-called "Turicel Social".