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Trip Mining and Recommendation from Geo-tagged Photos

Trip planning is generally a very time-consuming task due to the complex trip requirements and the lack of convenient tools/systems to assist the planning. In this paper, we propose a travel path search system based on geo-tagged photos to facilitate tourists' trip planning, not only for where to visit but also how to visit. The large scale geo-tagged photos that are public ally available on the web make this system possible, as geo-tagged photos encode rich travel-related metadata and can be used to mine travel paths from previous tourists. In this work, about 20 million geo-tagged photos were crawled from Panoramio.com. Then a substantial number of travel paths are minded from the crawled geo-tagged photos. After that, a search system is built to index and search the paths, and the Sparse Chamfer Distance is proposed to measure the similarity of two paths. The search system supports various types of queries, including (1) a destination name, (2) a user-specified region on the map, (3) some user-preferred locations. Based on the search system, users can interact with the system by specifying a region or several interest points on the map to find paths. Extensive experiments show the effectiveness of the proposed framework.