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Tag Correspondence Model for User Tag Suggestion

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Abstract

Some microblog services encourage users to annotate themselves with multiple tags, indicating their attributes and interests. User tags play an important role for personalized recommendation and information retrieval. In order to better understand the semantics of user tags, we propose Tag Correspondence Model (TCM) to identify complex correspondences of tags from the rich context of microblog users. The correspondence of a tag is referred to as a unique element in the context which is semantically correlated with this tag. In TCM, we divide the context of a microblog user into various sources (such as short messages, user profile, and neighbors). With a collection of users with annotated tags, TCM can automatically learn the correspondences of user tags from multiple sources. With the learned correspondences, we are able to interpret implicit semantics of tags. Moreover, for the users who have not annotated any tags, TCM can suggest tags according to users’ context information. Extensive experiments on a real-world dataset demonstrate that our method can efficiently identify correspondences of tags, which may eventually represent semantic meanings of tags.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Cun-Chao Tu, Zhi-Yuan Liu & Mao-Song Sun

  2. State Key Laboratory on Intelligent Technology and Systems, Tsinghua University, Beijing, 100084, China

    Cun-Chao Tu, Zhi-Yuan Liu & Mao-Song Sun

  3. National Laboratory for Information Science and Technology, Tsinghua University, Beijing, 100084, China

    Cun-Chao Tu, Zhi-Yuan Liu & Mao-Song Sun

  4. Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou, 221009, China

    Cun-Chao Tu, Zhi-Yuan Liu & Mao-Song Sun

Authors
  1. Cun-Chao Tu
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  2. Zhi-Yuan Liu
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  3. Mao-Song Sun
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Correspondence to Zhi-Yuan Liu.

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Tu, CC., Liu, ZY. & Sun, MS. Tag Correspondence Model for User Tag Suggestion. J. Comput. Sci. Technol. 30, 1063–1072 (2015). https://doi.org/10.1007/s11390-015-1582-6

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  • DOI: https://doi.org/10.1007/s11390-015-1582-6

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