Toward dynamic path recommender system based on social network data.

With the advancement of mobile technologies, more and more people are connected to social networks such as Facebook and Twitter. Social networks allow users to share diversity of information including spatio-temporal data either publicly or within their community of interest in realtime. Particularly, by analyzing social network data streams and then validating the content, one can extract knowledge about dynamic road conditions for a given city. This paper presents a dynamic path recommender system that helps users finding optimized routes in dynamic environments based on social network data. The system collects geo-tagged social network data from which relevant knowledge is extracted for identifying constraints such as accidents, weather conditions, and congestions. Moreover, by continuously collecting moving user's geo-tagged data, the system can also identify the traffic flow as well as roads' conditions. As soon as the system identifies and validates a given constraint, it can notify affected users and recommend an adapted route from their current position to the destination. A proof of concept of the system will be shown through three example scenarios.