A system for mining interesting tourist locations and travel sequences from public geo-tagged photos

Geo-tagged photos of users on social media sites (e.g., Flickr) provide plentiful location-based data. This data provide a wealth of information about user behaviours and their potential is increasing, as it becomes ever-more common for images to be associated with location information in the form of geo-tags. Recently, there is an increasing tendency to adopt the information from these geo-tagged photos for learning to recommend tourist locations. In this paper, we aim to propose a system to recommend interesting tourist locations and interesting tourist travel sequences (i.e., sequence of tourist locations) from a collection of geo-tagged photos. Proposed system is capable of understanding context (i.e., time, date, and weather), as well as taking into account the collective wisdom of people, to make tourist recommendations. We illustrate our technique on a sample of public Flickr data set. Experimental results demonstrate that the proposed approach is able to generate better recommendations as compared to other state-of-the-art landmark based recommendation methods.