Topic based context-aware travel recommendation method exploiting geotagged photos

The popularity of camera phones and photo sharing websites, 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 recommend travel locations in a city for a user, based on topic distribution of his travel histories in other cities and the given context (i.e., season and weather). A topic model is used to mine the interest distribution of users, which is then exploited to build the user-user similarity model and make travel recommendations. The season and weather context information is considered during the mining and the recommendation processes. Our method is evaluated on a Flickr dataset, which contains photos taken in 11 cities of China. Experimental results show the effectiveness of the proposed method in terms of the precision of travel behavior prediction.