A Study on Potential Head Advertisers in Sponsored Search

  • Changhao Jiang
  • Min Zhang
  • Bin Gao
  • Tie-Yan Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7675)


This paper studies the advertisers from whom the search engine may increase the revenue by offering an advanced sponsored search service. We divide them into head and tail advertisers according to their contributions to the search engine revenue. Data analysis shows that some tail advertisers have large amount of budgets and low budget usage ratios, who aimed to achieve the planned campaign goals (e.g., a large number of clicks), but they finally failed in doing so due to wrongly-selected bid keywords, inappropriate bid prices, and/or low-quality ad creatives. In this paper, we conduct a deep analysis on these advertisers. Specially, we define the measures to distinguish potential head advertisers from tail advertisers, and then run simulation experiments on the potential head advertisers by applying different improvements. Encouraging results have been achieved by our diagnosing approaches. We also show that a decision tree model can be implemented for a better improvement to those advertisers.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abrams, Z., Mendelevitch, O., Tomlin, J.: Optimal delivery of sponsored search advertisements subject to budget constraints. In: Proceedings of the 8th ACM Conference on Electronic Commerce, EC 2007, pp. 272–278. ACM, New York (2007)CrossRefGoogle Scholar
  2. 2.
    Anastasakos, T., Hillard, D., Kshetramade, S., Raghavan, H.: A collaborative filtering approach to ad recommendation using the query-ad click graph. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 1927–1930. ACM, New York (2009)CrossRefGoogle Scholar
  3. 3.
    Borgs, C., Chayes, J., Immorlica, N., Jain, K., Etesami, O., Mahdian, M.: Dynamics of bid optimization in online advertisement auctions. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 531–540. ACM, New York (2007)CrossRefGoogle Scholar
  4. 4.
    Borgs, C., Chayes, J., Immorlica, N., Mahdian, M., Saberi, A.: Multi-unit auctions with budget-constrained bidders. In: Proceedings of the 6th ACM Conference on Electronic Commerce, EC 2005, pp. 44–51. ACM, New York (2005)CrossRefGoogle Scholar
  5. 5.
    Chatterjee, P., Hoffman, D., Novak, T.: Modeling the clickstream: Implications for web-based advertising efforts. INFORMS on Marketing Science 22, 520–541 (2003)CrossRefGoogle Scholar
  6. 6.
    Dütting, P., Henzinger, M., Weber, I.: An expressive mechanism for auctions on the web. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 127–136. ACM, New York (2011)CrossRefGoogle Scholar
  7. 7.
    Feng, J., Bhargava, H.K., Pennock, D.M.: Implementing sponsored search in web search engines: Computational evaluation of alternative mechanisms. INFORMS J. on Computing 19, 137–148 (2007)CrossRefGoogle Scholar
  8. 8.
    Graepel, T., Candela, J., Borchert, T., Herbrich, R.: Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. In: Proceedings of the 27th International Conference on Machine Learning (2009)Google Scholar
  9. 9.
    Hillard, D., Schroedl, S., Manavoglu, E., Raghavan, H., Leggetter, C.: Improving ad relevance in sponsored search. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM 2010, pp. 361–370. ACM, New York (2010)CrossRefGoogle Scholar
  10. 10.
    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, KDD 2002, pp. 538–543. ACM, New York (2002)CrossRefGoogle Scholar
  11. 11.
    Kominers, S.D.: Dynamic Position Auctions with Consumer Search. In: Goldberg, A.V., Zhou, Y. (eds.) AAIM 2009. LNCS, vol. 5564, pp. 240–250. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    König, A.C., Gamon, M., Wu, Q.: Click-through prediction for news queries. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 347–354. ACM, New York (2009)CrossRefGoogle Scholar
  13. 13.
    Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)Google Scholar
  14. 14.
    Radlinski, F., Broder, A., Ciccolo, P., Gabrilovich, E., Josifovski, V., Riedel, L.: Optimizing relevance and revenue in ad search: a query substitution approach. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 403–410. ACM, New York (2008)CrossRefGoogle Scholar
  15. 15.
    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, WWW 2007, pp. 521–530. ACM, New York (2007)CrossRefGoogle Scholar
  16. 16.
    Wang, C., Zhang, P., Choi, R., D’Eredita, M.: Understanding consumers attitude toward advertising. In: Proceedings of the Eighth Americas Conference on Information Systems (2002)Google Scholar
  17. 17.
    Xu, W., Manavoglu, E., Cantu-Paz, E.: Temporal click model for sponsored search. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 106–113. ACM, New York (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Changhao Jiang
    • 1
  • Min Zhang
    • 1
  • Bin Gao
    • 2
  • Tie-Yan Liu
    • 2
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingP.R. China
  2. 2.Microsoft Research AsiaBeijingP.R. China

Personalised recommendations