Planning for travels is one of the most important activities everybody does while getting ready for it. In this research, we have reduced computational time and improved the quality of proposed routes and route score parameters in a social network and location based recommender system using sequential patterns in the graph. This improvement is gained by combining extracted data from locations from social networks, GPS data from taxi networks and creating a traffic aware system. First a network was created from points of interest (POI) extracted from social networks and GPS data from taxis. Then a two-level method was designed and implemented to personalize travel planning, increase route score, reduce computational time and improve proposed route and give the user the best possible recommendation.
This improvement is gained by combining extracted data from locations from social networks, GPS data from taxi networks and creating a traffic aware system. First a network was created from points of interest (POI) extracted from social networks and GPS data from taxis.