›› 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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[8] J¨aschke R, Marinho L B, Hotho A, Schmidt-Thieme L, Stumme G. Tag recommendations in social bookmarking systems. AI Communications, 2008, 21(4):231-247.

[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.

[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.

[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.

[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.

[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.

[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.

[15] Blei D, Ng A, Jordan M. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3:993-1022.

[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.

[17] Si X, Sun M. Tag-LDA for scalable real-time tag recommendation. Journal of Computational Information Systems, 2009, 6(1):23-31.

[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.

[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.

[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.

[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.

[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.

[23] Heinrich G. Parameter estimation for text analysis. Technical Report, vsonix GmbH + University of Leipzig, Germany, May 2005.

[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.

[25] Manning C D, Raghavan P, Schütze H. Introduction to Information Retrieval, Volume 1. Cambridge University Press, Cambridge, 2008.

[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.

[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.

[28] Cohn D, Chang H. Learning to probabilistically identify authoritative documents. In Proc. ICML, June 29-July 2, 2000, pp.167-174.
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