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Study on the Click Context of Web Search Users for Reliability Analysis

  • Rongwei Cen
  • Yiqun Liu
  • Min Zhang
  • Liyun Ru
  • Shaoping Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5839)

Abstract

User behavior information analysis has been shown important for optimization and evaluation of Web search and has become one of the major areas in both information retrieval and knowledge management researches. This paper focuses on users’ searching behavior reliability study based on large scale query and click-through logs collected from commercial search engines. The concept of reliability is defined in a probabilistic notion. The context of user click behavior on search results is analyzed in terms of relevance. Five features, namely query number, click entropy, first click ratio, last click ratio, and rank position, are proposed and studied to separate reliable user clicks from the others. Experimental results show that the proposed method evaluates the reliability of user behavior effectively. The AUC value of the ROC curve is 0.792, and the algorithm maintains 92.8% relevant clicks when filtering out 40% low quality clicks.

Keywords

User behavior analysis click reliability search user search engine 

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References

  1. 1.
    Yates, R., Tiberi, A.: Extracting semantic relations from query logs. In: Proceedings of the 13th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, pp. 76–85. ACM, New York (2007)CrossRefGoogle Scholar
  2. 2.
    Fuxman, A., Tsaparas, P., Achan, K., Agrawal, R.: Using the wisdom of the crowds for keyword generation. In: Proceeding of the 17th international Conference on World Wide Web, pp. 61–70. ACM, New York (2008)CrossRefGoogle Scholar
  3. 3.
    Joachims, T., Freitag, D., Mitchell, T.: WebWatcher: a tour guide for the world wide Web. In: IJCAI 1997, vol. 1, pp. 770–777. Morgan Kaufmann, San Francisco (1997)Google Scholar
  4. 4.
    Agichtein, E., Brill, E., Dumais, S., Ragno, R.: Learning user interaction models for predicting web search result preferences. In: Proceedings of the 29th ACM SIGIR Conference on Research and Development in information Retrieval, pp. 3–10. ACM, New York (2006)Google Scholar
  5. 5.
    Joachims, T., Granka, L., Pan, B., Hembrooke, H., Gay, G.: Accurately interpreting clickthrough data as implicit feedback. In: Proceedings of the 28th ACM SIGIR Conference on Research and Development in information Retrieval, pp. 154–161. ACM, New York (2005)Google Scholar
  6. 6.
    Tan, P., Kumar, V.: Modeling of web robot navigational patterns. In: Proceedings ACM WebKDD Workshop (2000)Google Scholar
  7. 7.
    Tan, P., Kumar, V.: Discovery of web robot sessions based on their navigational patterns. Data Mining and Knowledge Discovery 6, 9–35 (2002)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Yates, R., Hurtado, C., Mendoza, M., Dupret, G.: Modeling user search behavior. In: Proceedings of the 3th Latin American Web Congress. LA-WEB, p. 242. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar
  9. 9.
    Kammenhuber, N., Luxenburger, J., Feldmann, A., Weikum, G.: Web search clickstreams. In: Proceedings of the 6th ACM SIGCOMM Conference on internet Measurement, pp. 245–250. ACM, New York (2006)Google Scholar
  10. 10.
    Craswell, N., Zoeter, O., Taylor, M., Ramsey, B.: An experimental comparison of click position-bias models. In: Proceedings of the international Conference on Web Search and Web Data Mining, pp. 87–94. ACM, New York (2008)CrossRefGoogle Scholar
  11. 11.
    Guo, F., Liu, C., Wang, Y.M.: Efficient multiple-click models in web search. In: Proceedings of the 2nd ACM international Conference on Web Search and Data Mining, pp. 124–131. ACM, New York (2009)CrossRefGoogle Scholar
  12. 12.
    Liu, Y., Cen, R., Zhang, M., Ru, L., Ma, S.: Automatic Search Engine Evaluation Based On User Behavior Analysis. Journal of Software 19(11), 3023–3032 (2008)CrossRefGoogle Scholar
  13. 13.
    Agrawal, R., Halverson, A., Kenthapadi, K., Mishra, N., Tsaparas, P.: Generating labels from clicks. In: Baeza-Yates, R., Boldi, P., Ribeiro-Neto, B., Cambazoglu, B.B. (eds.) Proceedings of the 2nd ACM international Conference on Web Search and Data Mining (2009)Google Scholar
  14. 14.
    Yu, H., Liu, Y., Zhang, M., Ru, L., Ma, S.: Research in Search Engine User Behavior Based on Log Analysis. Journal of Chinese Information Processing 21(1), 109–114 (2007)CrossRefGoogle Scholar
  15. 15.
    Shannon, C.E.: A Mathematical Theory of Communication. Bell System Technical Journal 27, 379–423, 623–656 (1948)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Svore, K., Wu, Q., Burges, C., Raman, A.: Improving Web Spam Classification using Rank-time Features. In: Proceedings of AIRWeb 2007, pp. 9–16. ACM, New York (2007)Google Scholar
  17. 17.
    Sadagopan, N., Li, J.: Characterizing typical and atypical user sessions in clickstreams. In: Proceedings of the 17th international Conference on World Wide Web, pp. 885–894. ACM, New York (2008)Google Scholar
  18. 18.
    Agichtein, E., Brill, E., Dumais, S.: Improving web search ranking by incorporating user behavior information. In: Proceedings of the 29th Annual international ACM SIGIR Conference on Research and Development in information Retrieval, pp. 19–26. ACM, New York (2006)Google Scholar
  19. 19.
    Dou, Z., Song, R., Yuan, X., Wen, J.: Are click-through data adequate for learning web search rankings? In: Proceeding of the 17th ACM Conference on information and Knowledge Management, pp. 73–78. ACM, New York (2008)Google Scholar
  20. 20.
    Carletta, J.: Assessing Agreement on Classification Tasks: The Kappa Statistic. Computational Linguistics 22(2), 249–254 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rongwei Cen
    • 1
  • Yiqun Liu
    • 1
  • Min Zhang
    • 1
  • Liyun Ru
    • 1
  • Shaoping Ma
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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