Personalized Tag Recommendation

Social tagging becomes increasingly important to organize and search large-scale community-contributed photos on social websites. To facilitate generating high-quality social tags, tag recommendation by automatically assigning relevant tags to photos draws particular research interest. In this chapter, we focus on the personalized tag recommendation task and try to identify user-preferred, geo-location-specific as well as semantically relevant tags for a photo by leveraging rich contexts of the freely available community-contributed photos. For users and geo-locations, we assume they have different preferred tags assigned to a photo, and propose a subspace learning method to individually uncover the both types of preferences. Considering the visual feature is a lower level representation on semantics than the textual information, we adopt a progressive learning strategy by additionally introducing an intermediate subspace for the visual domain. Given an untagged photo with its geo-location to a user, the user-preferred and the geo-location-specific tags are found by the nearest neighbor search in the corresponding unified spaces.

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