Trip similarity computation for context-aware travel recommendation exploiting geotagged photos

The popularity of GPS-enabled digital cameras, smart phones, and photo sharing web sites, e.g. Flickr and Panoramio, has led to huge volumes of community-contributed geotagged photos (CCGPs) available on the Internet, which could be regarded as digital footprints of photo takers. In this paper, we propose a method to make context-aware and trip similarity based travel recommendations by mining CCGPs. We obtain user-specific travel preferences from the travel history of user in one city, and use these to recommend tourist locations in another city. The season and weather context are considered during the mining and the recommendation processes. The similarity of users is computed by the modified longest common subsequence and a user-location graph is built from their travel histories in one city, which is then exploited to make travel recommendations. Our method is evaluated on a Flickr dataset, which contains photos taken in four cities of China. Experimental results show the effectiveness of the proposed method.