Geotagged photos on social media like Flickr explicitly indicate the trajectories of tourists. They can be employed to reveal the tourists’ preference on landmarks and routings of tourism. Most of existing works on routing searches from social media are based on the trajectories of GPS-enabled devices’ users. We attempt to propose a novel approach in which the basic unit of routing is separate road segment instead of GPS trajectory segment. We introduce therefore a recommendation system that provides users with the most popular landmarks as well as the best travel routings between the landmarks. By using Flickr geotaggged photos, the top ranking travel destinations in a city can be identified and then the best travel routes between the popular travel destinations are recommended. We apply a spatial clustering method to identify the main travel landmarks and subsequently rank these landmarks. Using machine learning method, we calculate the tourism popularity of the road in terms of relevant parameters, e.g., the number of users and the number of Point-of-Interests. These popularity assessments are integrated into the routing recommendation system. This takes into consideration both the popularity assessment and the length of the road. Further information can be found in this new paper in the journal CEUS (Computers, Environment and Urban Systems).