Modeling User Expertise in Folksonomies by Fusing Multi-type Features

  • Junjie Yao
  • Bin Cui
  • Qiaosha Han
  • Ce Zhang
  • Yanhong Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6587)


The folksonomy refers to the online collaborative tagging system which offers a new open platform for content annotation with uncontrolled vocabulary. As folksonomies are gaining in popularity, the expert search and spammer detection in folksonomies attract more and more attention. However, most of previous work are limited on some folksonomy features. In this paper, we introduce a generic and flexible user expertise model for expert search and spammer detection. We first investigate a comprehensive set of expertise evidences related to users, objects and tags in folksonomies. Then we discuss the rich interactions between them and propose a unified Continuous CRF model to integrate these features and interactions. This model’s applications for expert recommendation and spammer detection are also exploited. Extensive experiments are conducted on a real tagging dataset and demonstrate the model’s advantages over previous methods, both in performance and coverage.


Average Precision User Expertise Expertise Model Expertise Evidence Expertise Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Balog, K., Azzopardi, L., de Rijke, M.: A language modeling framework for expert finding. Information Processing and Management 45(1), 1–19 (2009)CrossRefGoogle Scholar
  2. 2.
    Cui, B., Tung, A., Zhang, C., Zhao, Z.: Multiple feature fusion for social media applications. In: Proc. of ACM SIGMOD, pp. 435–446 (2010)Google Scholar
  3. 3.
    Deng, H., King, I., Lyu, M.R.: Enhancing expertise retrieval using community-aware strategies. In: Proc. of ACM CIKM, pp. 1733–1736 (2009)Google Scholar
  4. 4.
    Guan, Z., Bu, J., Mei, Q., Chen, C., Wang, C.: Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In: Proc. of ACM SIGIR, pp. 540–547 (2009)Google Scholar
  5. 5.
    Heymann, P., Koutrika, G., Garcia-Molina, H.: Fighting spam on social web sites: A survey of approaches and future challenges. IEEE Internet Computing 11(6), 36–45 (2007)CrossRefGoogle Scholar
  6. 6.
    Horowitz, D., Kamvar, S.: The anatomy of a large-scale social search engine. In: Proc. of WWW, pp. 431–440 (2010)Google Scholar
  7. 7.
    Krestel, R., Chen, L.: Using co-occurence of tags and resources to identify spammers. In: Proc. of ECML PKDD Discovery Challenge Workshop, pp. 38–46 (2008)Google Scholar
  8. 8.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proc. of ICML, pp. 282–289 (2001)Google Scholar
  9. 9.
    Madkour, A., Hefni, T., Hefny, A., Refaat, K.S.: Using semantic features to detect spamming in social bookmarking systems. In: Proc. of ECML PKDD Discovery Challenge Workshop, pp. 55–62 (2008)Google Scholar
  10. 10.
    Metzler, D., Croft, W.B.: A markov random field model for term dependencies. In: Proc. of ACM SIGIR, pp. 472–479 (2005)Google Scholar
  11. 11.
    Noll, M.G., Yeung, A.: et al. Telling experts from spammers: expertise ranking in folksonomies. In: Proc. of ACM SIGIR, pp. 612–619 (2009)Google Scholar
  12. 12.
    Qin, T., Liu, T., Zhang, X., Wang, D., Li, H.: Global ranking using continuous conditional random fields. In: Proc. of NIPS, pp. 1281–1288 (2008)Google Scholar
  13. 13.
    Sarkas, N., Das, G., Koudas, N.: Improved Search for Socially Annotated Data. PVLDB 2(1), 778–789 (2009)Google Scholar
  14. 14.
    Xin, X., King, I., Deng, H., Lyu, M.R.: A social recommendation framework based on multi-scale continuous conditional random fields. In: Proc. of ACM CIKM, pp. 1247–1256 (2009)Google Scholar
  15. 15.
    Xu, Z., Fu, Y., Mao, J., Su, D.: Towards the semantic web: collaborative tag suggestions. In: Proc. of WWW Collaborative Web Tagging Workshop (2006)Google Scholar
  16. 16.
    Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: Proc. of WWW, pp. 221–230 (2007)Google Scholar
  17. 17.
    Zhou, Y.H., Cong, G., Cui, B., Jensen, C.S., Yao, J.J.: Routing Questions to the Right Users in Online Communities. In: Proc. of ICDE, pp. 700–711 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Junjie Yao
    • 1
  • Bin Cui
    • 1
  • Qiaosha Han
    • 1
  • Ce Zhang
    • 2
  • Yanhong Zhou
    • 3
  1. 1.Department of Computer Science & Key Laboratory of High Confidence Software Technologies (Ministry of Education)Peking UniversityChina
  2. 2.Department of Computer ScienceUniversity of Wisconsin-MadisonChina
  3. 3.Yahoo! Global R&D CenterBeijingChina

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