›› 2015, Vol. 30 ›› Issue (5): 1063-1072.doi: 10.1007/s11390-015-1582-6

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Social Media Processing • Previous Articles     Next Articles

Tag Correspondence Model for User Tag Suggestion

Cun-Chao Tu(涂存超), Student Member, CCF, Zhi-Yuan Liu*(刘知远), Senior Member, CCF, Mao-Song Sun(孙茂松), Senior Member, CCF   

  1. 1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
    2 State Key Laboratory on Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China;
    3 National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;
    4 Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou 221009, China
  • Received:2014-11-15 Revised:2015-05-12 Online:2015-09-05 Published:2015-09-05
  • Contact: Zhi-Yuan Liu E-mail:liuzy@tsinghua.edu.cn
  • About author:Cun-Chao Tu is a Ph.D. student of the Department of Computer Science and Technology, Tsinghua University, Beijing. He got his B.E. degree in computer science from Tsinghua University in 2013. His research interests are user representation and social computation.
  • Supported by:

    This work is supported by the National Natural Science Foundation of China under Grant Nos. 61170196 and 61202140§and the Major Project of the National Social Science Foundation of China under Grant No. 13&ZD190.

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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