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With the recent popularity of social network services, a significant volume of heterogeneous social media data is generated by users, in the form of texts, photos, videos and collections of points of interest, etc. Such social media data provides users with rich resources for exploring content, such as looking for an interesting video or a favorite point of interest. However, the rapid growth of social media causes difficulties for users to efficiently retrieve their desired media items. Fortunately, users' digital footprints on social networks such as comments massively reflect individual's fine-grained preference on media items, that is, preference on different aspects of the media content, which can then be used for personalized social media search. In this article, we propose SESAME, a fine-grained preference-aware social media search framework leveraging user digital footprints on social networks. First, we collect users' direct …