In this paper, we aim to explore interesting landmark recommendations based on geo-tagged photos for each user. Meanwhile, we also try to answer such a question, i.e., when we want to go sightseeing in a large city such as Beijing, where should we go? To achieve our goal, first, we present a data field clustering method (DFCM). By using DFCM, we can cluster a large-scale geo-tagged web photo collection into groups (or landmarks) by location. And then, we provide more friendly and comprehensive overviews for each landmark. Subsequently, we model the users' dynamical behaviors using the fusion user similarity, which not only captures the overview semantic similarity, but also extract the trajectory similarity and the landmark trajectory similarity. Finally, we propose a personalized landmark recommendation algorithm based on the fusion user similarity. Experimental results show that our proposed approach can obtain a better performance than several state-of-the-art methods.
展开▼
