Skip to main content
SpringerLink
Log in

User interest mining via tags and bidirectional interactions on Sina Weibo

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Sina Weibo, one of the biggest social services in China, provides users with opportunities to share information and express their personal views, leading an explosive growth of information. How to recommend the right information to the proper person among massive data has received considerable critical attention in recent years. One of the main obstacles is the access to user topic interests. In this paper, we proposed an algorithm based on tags and bidirectional interactions to mine user topic interests on Sina Weibo. The algorithm, formulated by user interaction graph, fully takes advantage of the discordance between user interactions. Forward spread and back spread are thus utilized to update tag spread weights. We also quantify the impact of these two spread by tuning parameters on three sub data sets. In order to prove the superiority of the algorithm, we compare our algorithm with famous methods on Sina Weibo. The result demonstrates that our new algorithm outperforms other methods both in precision rate and recall rate, with the ability of mining user interest effectively with respect to tags and bidirectional interactions.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

References

  1. Blei D M, Ng A Y, Jordan M I, Latent dirichlet allocation, the Journal of machine Learning research, 3, 993–1022, (2003).

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

    MATH  Google Scholar 

  3. Cantador I, Bellogín A, Vallet D: Content-based Recommendation in Social Tagging Systems. In Proceedings of the Fourth ACM Conference on Recommender Systems. New York, pp. 237–240, (2010).

  4. China Internet Network Information Center. The 37th Statistical Report on Internet Development in China. (2016).

  5. Deng L, Huang J, Han Y, Zhou B, Liu Q: The Prediction of User Topic Interest Based on Tags and Interaction of Users. 2016 I.E. International Conference on Data Science in Cyberspace (ICDSC), (2016).

  6. Ding, Z., Jia, Y., Zhou, B., et al.: Mining topical influencers based on the multirelational network in micro-blogging sites. China Communications. 10(1), 93–104 (2013)

    Article  Google Scholar 

  7. Fan M, Zhou Q, Zheng T.F, “Mining the Personal Interests of Microbloggers via Exploiting Wikipedia Knowledge”, Computational Linguistics and Intelligent Text Processing. Springer, 2 vol. 2014, 188–200, (2014).

  8. GOLDER S H B A: The structure of collaborative tagging systems, Ithaca: Cornell University Library. (2005).

  9. Hotho A, Jäschke R, Schmitz C, et al, Information retrieval in folksonomies: Search and ranking, Springer, (2006).

  10. Katakis I, Tsoumakas G, Vlahavas I, Multilabel text classification for automated tag suggestion, ECML PKDD discovery challenge, pp. 75, (2008).

  11. Li, H., Yan, J., Weihong, H., et al.: Mining user interest in microblogs with a user-topic model. Communications, China. 11(8), 131–144 (2014)

    Article  Google Scholar 

  12. Liu, Z., Chen, X., Sun, M.: Mining the interests of Chinese microbloggers via keyword extraction. Frontiers of Computer Science in China. 6(1), 76–87 (2012)

    MathSciNet  Google Scholar 

  13. 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, (2010).

  14. Mihalcea R, Tarau P: TextRank: Bringing order into texts [C]. (2004).

  15. Nie Y, Huang J, Li A, et al.: Identifying users based on behavioral-modeling across social media sites. Asia-Pacific Web Conference. Springer International Publishing: 48–55, (2014)

  16. Nie, Y., Jia, Y., Li, S., et al.: Identifying users across social networks based on dynamic core interests. Neurocomputing. 210, 107–115 (2016)

    Article  Google Scholar 

  17. Ohkura T, Kiyota Y, Nakagawa H: Browsing system for weblog articles based on automated folksonomy. In Proceedings of the WWW 2006 Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, WWW. (2006).

  18. Porteous Ian, Newman David, Ihler Alexander, Asuncion Arthur, Smyth Padhraic, Welling Max: Fast collapsed gibbs sampling for latent dirichlet allocation, SIGKDD, (2008).

  19. Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., et al.: Learning author-topic models from text corpora. ACM Transactions on Information Systems (TOIS). 28(1), 4 (2010)

    Article  Google Scholar 

  20. Steinbach M, Kapypis G, Kumar V: A Comparison of Document C1ustering Techniques. Proceedings of KDD Workshop on Text Mining, pp. 109–111, (2000).

  21. Wang, X., Jia, Y., Zhou, B., et al.: Interaction relation based user tag prediction in microblogging site. Computer Engineering & Science. 35(10), 44–50 (2013)

    Google Scholar 

  22. Wanga X, Jia Y, Chen RH, Zhou B: Ranking User Tags in Micro-blogging Website. 2015 International Conference on Information Science and Control Engineering, pp. 400–403, (2015).

  23. Xiang, W., Jia, Y., Chen, R.H., et al.: Improving text categorization with semantic knowledge in Wikipedia. IEICE Trans. Inf. Syst. E96-D(12), 2786–2794 (2013)

    Article  Google Scholar 

  24. Xu, Z., Fu, Y., Mao, J., et al.: “towards the semantic Web: collaborative tag suggestions”, in collaborative Web tagging workshop WWW2006. Edinburgh, Scotland (2006)

    Google Scholar 

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

  26. Zhang, B., Zhang, Y., Gao, K.-N., et al.: Combining relation and content analysis for social tagging recommendation. Ruanjian Xuebao/Journal of Software. 23(3), 476–488 (2012)

    Google Scholar 

Download references

Acknowledgements

The authors would thank their colleagues, past and present, who contributed to the research described in this paper. The work described in this paper is partially supported by National Key Fundamental Research and Development Program (No.2013CB329601, No.2013CB329602, No.2013CB329604) and National Natural Science Foundation of China (No.61502517, No.61372191, No.61572492), 863 Program of China (Grant No. 2012AA01A401, 2012AA01A402, 2012AA013002), Project funded by China Postdoctoral Science Foundation (2013 M542560, 2015 T81129)

Author information

Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410073, China

    Lu Deng, Yan Jia, Bin Zhou & Jiuming Huang

  2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100000, China

    Yi Han

Authors
  1. Lu Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiuming Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yi Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lu Deng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, L., Jia, Y., Zhou, B. et al. User interest mining via tags and bidirectional interactions on Sina Weibo. World Wide Web 21, 515–536 (2018). https://doi.org/10.1007/s11280-017-0469-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-017-0469-6

Keywords

Search

Navigation