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Recent years have witnessed great prosperity in community-contributed multimedia. Discovering, extracting, and summarizing knowledge from these data enables us to make better sense of the world. In this paper, we report our work on mining famous city landmarks from blogs for personalized tourist suggestions. Our main contribution is a graph modeling framework to discover city landmarks by mining blog photo correlations with community supervision. This modeling fuses context, content, and community information in a style that simulates both static (PageRank) and dynamic (HITS) ranking models to highlight representative data from the consensus of blog users.
Ji et al. (2009) studied this question.