World Wide Web

, Volume 17, Issue 5, pp 1081–1107 | Cite as

Ranking user authority with relevant knowledge categories for expert finding

  • Hengshu Zhu
  • Enhong Chen
  • Hui Xiong
  • Huanhuan Cao
  • Jilei Tian
Article

Abstract

The problem of expert finding targets on identifying experts with special skills or knowledge for some particular knowledge categories, i.e. knowledge domains, by ranking user authority. In recent years, this problem has become increasingly important with the popularity of knowledge sharing social networks. While many previous studies have examined authority ranking for expert finding, they have a focus on leveraging only the information in the target category for expert finding. It is not clear how to exploit the information in the relevant categories of a target category for improving the quality of authority ranking. To that end, in this paper, we propose an expert finding framework based on the authority information in the target category as well as the relevant categories. Along this line, we develop a scalable method for measuring the relevancies between categories through topic models, which takes consideration of both content and user interaction based category similarities. Also, we provide a topical link analysis approach, which is multiple-category-sensitive, for ranking user authority by considering the information in both the target category and the relevant categories. Finally, in terms of validation, we evaluate the proposed expert finding framework in two large-scale real-world data sets collected from two major commercial Question Answering (Q&A) web sites. The results show that the proposed method outperforms the baseline methods with a significant margin.

Keywords

Authority ranking Expert finding Category relevancy Link analysis Question answering 

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References

  1. 1.
    Azzopardi, L., Girolami, M., Risjbergen, K.V.: Investigating the relationship between language model perplexity and ir precision-recall measures. In: Proceedings of the 26th International Conference on Research and Development in Information Retrieval (SIGIR’03), pp. 369–370 (2003)Google Scholar
  2. 2.
    Balog, K., Azzopardi, L., de Rijke, M.: A language modeling framework for expert finding. Inf. Process. Manag. 45, 1–19 (2009)CrossRefGoogle Scholar
  3. 3.
    Balog, K., Azzopardi, L., Rijke, M.D.: Formal models for expert finding in enterprise corpora. In: Research and Development in Information Retrieval, pp. 43–50 (2006)Google Scholar
  4. 4.
    Bao, T., Cao, H., Chen, E., Tian, J., Xiong, H.: An unsupervised approach to modeling personalized contexts of mobile users. In: ICDM’10, pp. 38–47 (2010)Google Scholar
  5. 5.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Lantent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  6. 6.
    Bouguessa, M., Dumoulin, B., Wang, S.: Identifying authoritative actors in question-answering forums: the case of Yahoo! answers. In: Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’08, pp. 866–874. ACM, New York (2008)Google Scholar
  7. 7.
    Campbell, C.S., Maglio, P.P., Cozzi, A., Dom, B.: Expertise identification using email communications. In: Proceedings of the 12th International Conference on Information and Knowledge Management, CIKM ’03, pp. 528–531. ACM, New York (2003)Google Scholar
  8. 8.
    Dom, B., Eiron, I., Cozzi, A., Zhang, Y.: Graph-based ranking algorithms for e-mail expertise analysis. In: Proceedings of the 8th ACM SIGMOD Sorkshop on Research Issues in Data Mining and Knowledge Discovery, DMKD ’03, pp. 42–48. ACM, New York (2003)CrossRefGoogle Scholar
  9. 9.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. USA 101, 5228–5235 (2004)CrossRefGoogle Scholar
  10. 10.
    Heinrich, G.: Parameter estimation for text analysis. Technical report, University of Lipzig (2009)Google Scholar
  11. 11.
    Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’99, pp. 50–57. ACM, New York (1999)CrossRefGoogle Scholar
  12. 12.
    Huang, A.: Similarity measures for text document clustering. In: Proceedings of the 6th New Zealand Computer Science Research Student Conference (NZCSRSC2008), pp. 49–56. Christchurch, New Zealand (2008)Google Scholar
  13. 13.
    Jiang, J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: In ROCLING X, pp. 19–33 (1997)Google Scholar
  14. 14.
    Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: Proceedings of the 16th ACM Conference on Information and Knowledge Management, CIKM ’07, pp. 919–922. ACM, New York (2007)Google Scholar
  15. 15.
    Kao, W.C., Liu, D.R., Wang, S.W.: Expert finding in question-answering websites: a novel hybrid approach. In: Proceedings of the 2010 ACM Symposium on Applied Computing, SAC ’10, pp. 867–871. ACM, New York (2010)CrossRefGoogle Scholar
  16. 16.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Kullback, S., Leibler, R.A.: On Information and Sufficiency, pp. 79–86 (1951)Google Scholar
  18. 18.
    Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09, pp. 467–476. ACM, New York (2009)CrossRefGoogle Scholar
  19. 19.
    Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM ’10, pp. 199–208. ACM, New York (2010)CrossRefGoogle Scholar
  20. 20.
    Liu, X., Croft, W.B., Koll, M.: Finding experts in community-based question-answering services. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM ’05, pp. 315–316. ACM, New York (2005)Google Scholar
  21. 21.
    Liu, Y., Bian, J., Agichtein, E.: Predicting information seeker satisfaction in community question answering. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’08, pp. 483–490. ACM, New York (2008)CrossRefGoogle Scholar
  22. 22.
    Lu, Y., Quan, X., Ni, X., Liu, W., Xu, Y.: Latent link analysis for expert finding in user-interactive question answering services. In: Proceedings of the 5th International Conference on Semantics, Knowledge and Grid, SKG ’09, pp. 54–59. IEEE (2009)Google Scholar
  23. 23.
    McCallum, A., Corrada-Emmanuel, A., Wang, X.: Topic and role discovery in social networks. In: Proceedings of the 16th International Joint Conferences on Artificial Intelligence, IJCAI ’05, pp. 786–791 (2005)Google Scholar
  24. 24.
    Nie, L., Davison, B.D., Qi, X.: Topical link analysis for web search. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’06, pp. 91–98. ACM, New York (2006)CrossRefGoogle Scholar
  25. 25.
    Nie, L., Davison, B.D., Wu, B.: From whence does your authority come? Utilizing community relevance in ranking. In: Proceedings of the 22nd National Conference on Artificial Intelligence, AAAI ’07, vol. 2, pp. 1421–1426. AAAI Press (2007)Google Scholar
  26. 26.
    Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using em. Mach. Learn. 39, 103–134 (2000)CrossRefMATHGoogle Scholar
  27. 27.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. In: Stanford Digital Library Technical Report (1998)Google Scholar
  28. 28.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18, 613–620 (1975)CrossRefMATHGoogle Scholar
  29. 29.
    Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proceedings of the Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09, pp. 807–816. ACM, New York (2009)CrossRefGoogle Scholar
  30. 30.
    tau Yih, W., Toutanova, K., Platt, J., Meek, C.: Learning discriminative projections for text similarity measures. In: Proceedings of the 15th Conference on Computational Natural Language Learning, CoNLL’11 (2013)Google Scholar
  31. 31.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press (2002)Google Scholar
  32. 32.
    Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, WSDM ’10, pp. 261–270. ACM, New York (2010)CrossRefGoogle Scholar
  33. 33.
    Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th International Conference on World Wide Web, WWW ’07, pp. 221–230. ACM, New York (2007)CrossRefGoogle Scholar
  34. 34.
    Zhang, J., Tang, J., Li, J.: Expert finding in a social network. In: Proceedings of the 12th International Conference on Database Systems for Advanced Applications, DASFAA ’07, pp. 1066–1069. Springer (2007)Google Scholar
  35. 35.
    Zhu, H., Cao, H., Xiong, H., Chen, E., Tian, J.: Towards expert finding by leveraging relevant categories in authority ranking. In: Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM ’11 (2011)Google Scholar
  36. 36.
    Zhu, J., Huang, X., Song, D., Ruger, S.: Integrating multiple document features in language models for expert finding. Knowl. Inf. Syst. 23, 29–54 (2010)CrossRefGoogle Scholar
  37. 37.
    Zhu, H., Chen, E., Cao, H.: Finding experts in tag based knowledge sharing communities. In: Proceedings of the 5th International Conference on Knowledge Science, Engineering and Management, KSEM’11, pp. 183–195. Springer, Berlin (2011). doi:10.1007/978-3-642-25975-3_17 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Hengshu Zhu
    • 1
  • Enhong Chen
    • 1
  • Hui Xiong
    • 3
  • Huanhuan Cao
    • 2
  • Jilei Tian
    • 2
  1. 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.NokiaBeijingChina
  3. 3.Management Science and Information Systems Department, Rutgers Business SchoolRutgers UniversityNewarkUSA

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