Advertisement

Incorporating Position Bias into Click-Through Bipartite Graph

  • Rongjie Cai
  • Cheng Luo
  • Yiqun LiuEmail author
  • Shaoping Ma
  • Min Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10390)

Abstract

Click-through bipartite graph has been regarded as an effective method in search user behavior analysis researches. In most existing bipartite graph construction studies, user clicks are treated as equally important. However, considering the existence of position bias factor in user click-through behavior, clicks on results in different ranking positions should be treated separately. In this work, we choose a classical click-through bipartite graph model, which named label propagation model, and evaluate the improvement of performance by considering the effect of position bias. We propose three hypotheses to explain the influence of position bias, and modify the formulas of label propagation algorithm. We use AUC as the evaluation metric, which express the effectiveness of spam URLs identification by label propagation algorithm and its improved methods. The experimental results demonstrate that the proposed methods work better than the baseline method.

Keywords

Position bias Click-through bipartite graph Label propagation model 

References

  1. 1.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Wu, W., Li, H., Xu, J.: Learning query and document similarities from click-through bipartite graph with metadata. In: Proceedings of the sixth ACM International Conference on Web Search and Data Mining, pp. 687–696. ACM (2013)Google Scholar
  3. 3.
    Wei, C., Liu, Y., Zhang, M., Ma, S., Ru, L., Zhang, K.: Fighting against web spam: a novel propagation method based on click-through data. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 395–404. ACM (2012)Google Scholar
  4. 4.
    Luo, C., Liu, Y., Ma, S., Zhang, M., Ru, L., Zhang, K.: Pornography detection with the wisdom of crowds. In: Banchs, R.E., Silvestri, F., Liu, T.-Y., Zhang, M., Gao, S., Lang, J. (eds.) AIRS 2013. LNCS, vol. 8281, pp. 227–238. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-45068-6_20 CrossRefGoogle 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 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 154–161. ACM (2005)Google Scholar
  6. 6.
    Craswell, N., Zoeter, O., Taylor, M., Ramsey, B.: An experimental comparison of click position-bias models. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 87–94. ACM (2008)Google Scholar
  7. 7.
    Jiang, S., Hu, Y., Kang, C., Daly Jr., T., Yin, D., Chang, Y., Zhai, C.: Learning query and document relevance from a web-scale click graph. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 185–194. ACM (2016)Google Scholar
  8. 8.
    Xue, G.R., Zeng, H.J., Chen, Z., Yu, Y., Ma, W.Y., Xi, W., Fan, W.: Optimizing web search using web click-through data. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, pp. 118–126. ACM (2004)Google Scholar
  9. 9.
    Li, X., Wang, Y.Y., Acero, A.: Learning query intent from regularized click graphs. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 339–346. ACM (2008)Google Scholar
  10. 10.
    Yi, J., Maghoul, F.: Query clustering using click-through graph. In: Proceedings of the 18th International Conference on World Wide Web, pp. 1055–1056. ACM (2009)Google Scholar
  11. 11.
    Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th International Conference on World Wide Web, pp. 521–530. ACM (2007)Google Scholar
  12. 12.
    Agichtein, E., Brill, E., Dumais, S., Ragno, R.: Learning user interaction models for predicting web search result preferences. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3–10. ACM (2006)Google Scholar
  13. 13.
    Antonellis, I., Molina, H.G., Chang, C.C.: Simrank++: query rewriting through link analysis of the click graph. Proc. VLDB Endowment 1(1), 408–421 (2008)CrossRefGoogle Scholar
  14. 14.
    Jeh, G., Widom, J.: SimRank : a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM (2002)Google Scholar
  15. 15.
    Cao, H., Jiang, D., Pei, J., He, Q., Liao, Z., Chen, E., Li, H.: Context-aware query suggestion by mining click-through and session data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 875–883. ACM (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Rongjie Cai
    • 1
  • Cheng Luo
    • 1
  • Yiqun Liu
    • 1
    Email author
  • Shaoping Ma
    • 1
  • Min Zhang
    • 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

Personalised recommendations