Skip to main content
SpringerLink
Log in

Dynamic User Attribute Discovery on Social Media

  • Conference paper
  • First Online:
Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9931))

Included in the following conference series:

Abstract

Social media service defines a new paradigm of people communicating, self-expressing and sharing on the Web. Users in today’s social media platforms often post contents, inferring their interests/attributes, which are significant for many Web services such as social recommendation, personalized searching and online advertising. User attributes are temporally dynamic along with internal interest changing and external influence. Based on topic modeling, we present a probabilistic method for dynamic user attribute discovery. Our method automatically detects user attributes and models the dynamics using time windows and decay function, thereby facilitating more accurate recommendation. Evaluation on a Sina Weibo dataset shows the superiority in terms of precision, recall and F-measure as compared to baselines, such as static user attribute modeling.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.twitter.com/.

  2. 2.

    http://www.pinterest.com/.

  3. 3.

    http://www.weibo.com/.

References

  1. Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Semantic enrichment of Twitter posts for user profile construction on the social web. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 375–389. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  2. Abel, F., Herder, E., Houben, G.J., Henze, N., Krause, D.: Cross-system user modeling and personalization on the social web. User Model. User-Adap. Inter. 23(2–3), 169–209 (2013)

    Article  Google Scholar 

  3. Asuncion, A., Welling, M., Smyth, P., Teh, Y.W.: On smoothing and inference for topic models. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. pp. 27–34. AUAI Press (2009)

    Google Scholar 

  4. Bhattacharya, P., Zafar, M.B., Ganguly, N., Ghosh, S., Gummadi, K.P.: Inferring user interests in the Twitter social network. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 357–360. ACM (2014)

    Google Scholar 

  5. Bian, J., Yang, Y., Chua, T.S.: Multimedia summarization for trending topics in microblogs. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 1807–1812. ACM (2013)

    Google Scholar 

  6. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  7. Chen, J., Wang, C., Wang, J.: A personalized interest-forgetting Markov model for recommendations. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)

    Google Scholar 

  8. Ding, Y., Jiang, J.: Extracting interest tags from Twitter user biographies. In: Jaafar, A., Mohamad Ali, N., Mohd Noah, S.A., Smeaton, A.F., Bruza, P., Bakar, Z.A., Jamil, N., Sembok, T.M.T. (eds.) AIRS 2014. LNCS, vol. 8870, pp. 268–279. Springer, Heidelberg (2014)

    Google Scholar 

  9. Gao, Q., Abel, F., Houben, G.J., Tao, K.: Interweaving trend and user modeling for personalized news recommendation. In: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 100–103. IEEE Computer Society (2011)

    Google Scholar 

  10. Geng, X., Zhang, H., Song, Z., Yang, Y., Luan, H., Chua, T.S.: One of a kind: user profiling by social curation. In: Proceedings of the ACM International Conference on Multimedia, pp. 567–576. ACM (2014)

    Google Scholar 

  11. Lim, K.H., Datta, A.: Interest classification of Twitter users using Wikipedia. In: Proceedings of the 9th International Symposium on Open Collaboration, p. 22. ACM (2013)

    Google Scholar 

  12. Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on Twitter: a first look. In: Proceedings of the Fourth Workshop on Analytics for Noisy Unstructured Text Data, pp. 73–80. ACM (2010)

    Google Scholar 

  13. He, W., Liu, H., He, J., Tang, S., Du, X.: Extracting interest tags for non-famous users in social network. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 861–870. ACM (2015)

    Google Scholar 

  14. Ottoni, R., Las Casas, D.B., Pesce, J.P., Meira Jr., W., Wilson, C., Mislove, A., Almeida, V.: Of pins and tweets: investigating how users behave across image-and text-based social networks (2014)

    Google Scholar 

  15. Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, pp. 487–494. AUAI Press (2004)

    Google Scholar 

  16. Sen, W., Xiaonan, Z., Yannan, D.: A collaborative filtering recommender system integrated with interest drift based on forgetting function. Int. J. u- and e- Serv. Sci. Technol. 8(4), 247–264 (2015)

    Article  Google Scholar 

  17. Wang, T., Liu, H., He, J., Du, X.: Mining user interests from information sharing behaviors in social media. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part II. LNCS, vol. 7819, pp. 85–98. Springer, Heidelberg (2013)

    Google Scholar 

  18. Xu, Z., Lu, R., Xiang, L., Yang, Q.: Discovering user interest on Twitter with a modified author-topic model. In: 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 422–429. IEEE (2011)

    Google Scholar 

  19. Yan, X., Guo, J., Lan, Y., Cheng, X.: A biterm topic model for short texts. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1445–1456. International World Wide Web Conferences Steering Committee (2013)

    Google Scholar 

  20. Yin, H., Cui, B., Chen, L., Hu, Z., Huang, Z.: A temporal context-aware model for user behavior modeling in social media systems. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 1543–1554. ACM (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Xiu Huang, Yang Yang, Yue Hu, Fumin Shen & Jie Shao

Authors
  1. Xiu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yue Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fumin Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yang Yang .

Editor information

Editors and Affiliations

  1. School of Computing, University of Utah, Salt Lake City, Utah, USA

    Feifei Li

  2. School of Electrical Engineering, Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. Soochow University , Suzhou, China

    Kai Zheng

  4. Soochow University , Suzhou, China

    Guanfeng Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Huang, X., Yang, Y., Hu, Y., Shen, F., Shao, J. (2016). Dynamic User Attribute Discovery on Social Media. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45814-4_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45813-7

  • Online ISBN: 978-3-319-45814-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation