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 LiuEmail author
  • Mao-Song Sun
Regular Paper


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.


microblog user tag suggestion tag correspondence model probabilistic graphical model context 


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  1. [1]
    McPherson M, Smith-Lovin L, Cook J M. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 2001, 27: 415–444.CrossRefGoogle Scholar
  2. [2]
    Liang H, Xu Y, Li Y, Nayak R, Tao X. Connecting users and items with weighted tags for personalized item recommendations. In Proc. the 21st ACM Conference on Hypertext and Hypermedia, June 2010, pp.51-60.Google Scholar
  3. [3]
    Peng J, Zeng D, Zhao H, Wang F. Collaborative filtering in social tagging systems based on joint item-tag recommendations. In Proc. the 19th ACM International Conference on Information and Knowledge Management, Oct. 2010, pp.809-818.Google Scholar
  4. [4]
    Zhen Y, Li W, Yeung D. TagiCoFi: Tag informed collaborative filtering. In Proc. the 3rd ACM Conference on Recommender Systems, Oct. 2009, pp.69-76.Google Scholar
  5. [5]
    Symeonidis P, Nanopoulos A, Manolopoulos Y. Tag recommendations based on tensor dimensionality reduction. In Proc. the 2008 ACM Conference on Recommender Systems, Oct. 2008, pp.43-50.Google Scholar
  6. [6]
    Rendle S, Marinho L B, Nanopoulos A, Schmidt-Thieme L. Learning optimal ranking with tensor factorization for tag recommendation. In Proc. the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28-July 1, 2009, pp.727-736.Google Scholar
  7. [7]
    Rendle S, Schmidt-Thieme L. Pairwise interaction tensor factorization for personalized tag recommendation. In Proc. the 3rd ACM International Conference on Web Search and Data Mining, Feb. 2010, pp.81-90.Google Scholar
  8. [8]
    Jäschke R, Marinho L B, Hotho A, Schmidt-Thieme L, Stumme G. Tag recommendations in social bookmarking systems. AI Communications, 2008, 21(4): 231–247.zbMATHMathSciNetGoogle Scholar
  9. [9]
    Ohkura T, Kiyota Y, Nakagawa H. Browsing system for weblog articles based on automated folksonomy. In Proc. the 15th International Conference on World Wide Web, May 2006.Google Scholar
  10. [10]
    Mishne G. AutoTag: A collaborative approach to automated tag assignment for weblog posts. In Proc. the 15th International Conference on World Wide Web, May 2006, pp.953-954.Google Scholar
  11. [11]
    Lee S, Chun A. Automatic tag recommendation for the Web 2.0 blogosphere using collaborative tagging and hybrid ANN semantic structures. In Proc. the 6th WSEAS International Conference on Applied Computer Science, Apr. 2007, pp.88-93.Google Scholar
  12. [12]
    Katakis I, Tsoumakas G, Vlahavas I. Multilabel text classification for automated tag suggestion. In Proc. the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, volume 18, Sept. 2008.Google Scholar
  13. [13]
    Fujimura S, Fujimura K, Okuda H. Blogosonomy: Autotagging any text using bloggers’ knowledge. In Proc. IEEE/WIC/ACM International Conference on Web Intelligence, Nov. 2007, pp.205-212.Google Scholar
  14. [14]
    Heymann P, Ramage D, Garcia-Molina H. Social tag prediction. In Proc. the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2008, pp.531-538.Google Scholar
  15. [15]
    Blei D, Ng A, Jordan M. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993–1022.zbMATHGoogle Scholar
  16. [16]
    Krestel R, Fankhauser P, Nejdl W. Latent Dirichlet allocation for tag recommendation. In Proc. the 3rd ACM Conference on Recommender Systems, Oct. 2009, pp.61-68.Google Scholar
  17. [17]
    Si X, Sun M. Tag-LDA for scalable real-time tag recommendation. Journal of Computational Information Systems, 2009, 6(1): 23–31.CrossRefGoogle Scholar
  18. [18]
    Liu Z, Tu C, Sun M. Tag dispatch model with social network regularization for microblog user tag suggestion. In Proc. the 24th International Conference on Computational Linguistics, Dec. 2012, pp.755-764.Google Scholar
  19. [19]
    Bundschus M, Yu S, Tresp V, Rettinger A, Dejori M, Kriegel H. Hierarchical Bayesian models for collaborative tagging systems. In Proc. the 9th IEEE International Conference on Data Mining, Dec. 2009, pp.728-733.Google Scholar
  20. [20]
    Iwata T, Yamada T, Ueda N. Modeling social annotation data with content relevance using a topic model. In Proc. the 23rd Annual Conference on Neural Information Processing Systems, Dec. 2009, pp.835-843.Google Scholar
  21. [21]
    Blei D, Jordan M. Modeling annotated data. In Proc. the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28- August 1, 2003, pp.127-134.Google Scholar
  22. [22]
    Griffiths T, Steyvers M. Finding scientific topics. Proc. the National Academy of Sciences of the United States of America, 2004, 101(Suppl 1): 5228–5235.CrossRefGoogle Scholar
  23. [23]
    Heinrich G. Parameter estimation for text analysis. Technical Report, vsonix GmbH + University of Leipzig, Germany, May 2005.Google Scholar
  24. [24]
    Andrieu C, de Freitas N, Doucet A, Jordan M. An introduction to MCMC for machine learning. Machine Learning, 2003, 50(1/2): 5–43.zbMATHCrossRefGoogle Scholar
  25. [25]
    Manning C D, Raghavan P, Sch¨utze H. Introduction to Information Retrieval, Volume 1. Cambridge University Press, Cambridge, 2008.Google Scholar
  26. [26]
    Mei Q, Cai D, Zhang D, Zhai C. Topic modeling with network regularization. In Proc. the 17th International Conference on World Wide Web, Apr. 2008, pp.101-110.Google Scholar
  27. [27]
    Chang J, Blei D M. Relational topic models for document networks. In Proc. the 12th International Conference on Artificial Intelligence and Statistics, Apr. 2009, pp.81-88.Google Scholar
  28. [28]
    Cohn D, Chang H. Learning to probabilistically identify authoritative documents. In Proc. ICML, June 29-July 2, 2000, pp.167-174.Google Scholar

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
    Email author
  • 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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