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Journal of Computer Science and Technology

, Volume 30, Issue 5, pp 1063–1072 | Cite as

Tag Correspondence Model for User Tag Suggestion

  • Cun-Chao Tu
  • Zhi-Yuan Liu
  • Mao-Song Sun
Regular Paper

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.

Keywords

microblog user tag suggestion tag correspondence model probabilistic graphical model context 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Cun-Chao Tu
    • 1
    • 2
    • 3
    • 4
  • Zhi-Yuan Liu
    • 1
    • 2
    • 3
    • 4
  • Mao-Song Sun
    • 1
    • 2
    • 3
    • 4
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.State Key Laboratory on Intelligent Technology and SystemsTsinghua UniversityBeijingChina
  3. 3.National Laboratory for Information Science and TechnologyTsinghua UniversityBeijingChina
  4. 4.Jiangsu Collaborative Innovation Center for Language AbilityJiangsu Normal UniversityXuzhouChina

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