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. 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 the 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Blei, D., Jordan, M.: Modeling annotated data. In: Proceedings of SIGIR, pp. 127–134. ACM (2003)
Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. JMLR 3, 993–1022 (2003)
Bundschus, M., Yu, S., Tresp, V., Rettinger, A., Dejori, M., Kriegel, H.: Hierarchical bayesian models for collaborative tagging systems. In: Proceedings of ICDM, pp. 728–733 (2009)
Chang, J., Blei, D.M.: Relational topic models for document networks. In: Proceedings of AISTATS, pp. 81–88 (2009)
Cohn, D., Chang, H.: Learning to probabilistically identify authoritative documents. In: Proceedings of ICML, pp. 167–174 (2000)
Griffiths, T., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America 101(suppl. 1), 5228–5235 (2004)
Iwata, T., Yamada, T., Ueda, N.: Modeling social annotation data with content relevance using a topic model. In: Proceedings of NIPS, pp. 835–843 (2009)
Jaschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in social bookmarking systems. AI Communications 21(4), 231–247 (2008)
Katakis, I., Tsoumakas, G., Vlahavas, I.: Multilabel text classification for automated tag suggestion. In: ECML PKDD Discovery Challenge 2008, p. 75 (2008)
Liu, Z., Chen, X., Sun, M.: A simple word trigger method for social tag suggestion. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1577–1588. Association for Computational Linguistics (2011)
Liu, Z., Tu, C., Sun, M.: Tag dispatch model with social network regularization for microblog user tag suggestion. In: 24th International Conference on Computational Linguistics, p. 755. Citeseer (2012)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval, vol. 1. Cambridge University Press, Cambridge (2008)
Mei, Q., Cai, D., Zhang, D., Zhai, C.: Topic modeling with network regularization. In: Proceedings of WWW, pp. 101–110 (2008)
Peng, J., Zeng, D., Zhao, H., Wang, F.: Collaborative filtering in social tagging systems based on joint item-tag recommendations. In: Proceedings of CIKM, pp. 809–818. ACM (2010)
Rendle, S., Balby Marinho, L., Nanopoulos, A., Schmidt-Thieme, L.: Learning optimal ranking with tensor factorization for tag recommendation. In: Proceedings of KDD, pp. 727–736. ACM (2009)
Si, X., Sun, M.: Tag-LDA for scalable real-time tag recommendation. Journal of Computational Information Systems 6(1), 23–31 (2009)
Si, X., Liu, Z., Sun, M.: Modeling social annotations via latent reason identification (2010)
Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: Proceedings of RecSys, pp. 43–50. ACM (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tu, C., Liu, Z., Sun, M. (2014). Inferring Correspondences from Multiple Sources for Microblog User Tags. In: Huang, H., Liu, T., Zhang, HP., Tang, J. (eds) Social Media Processing. SMP 2014. Communications in Computer and Information Science, vol 489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45558-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-662-45558-6_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45557-9
Online ISBN: 978-3-662-45558-6
eBook Packages: Computer ScienceComputer Science (R0)