Skip to main content
SpringerLink
Log in

An Adaptive Pricing Scheme in Sponsored Search Auction: A Hierarchical Fuzzy Classification Approach

  • Conference paper
Advances in Computing and Information Technology (ACITY 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 198))

  • 1758 Accesses

Abstract

Sponsored Search Auctions (SSA) are gaining widespread attention in web commerce community because of their highly targeted customers and billion dollars revenue generating online market. Unlike other form of auctions this class possesses fairly complex interaction among its key players, Users, Advertisers and Search Engines. Therefore research issues pertaining to SSA are being explored with large momentum in eclectic domains e.g. game theory, algorithmic theory and machine learning etc. Though problems related to different pricing schemes in SSA need more focus from researchers especially in analyzing adaptive pricing measures .This work is an effort towards making diligent use of information available in terms of different auctions’ situations by ingraining best of major popular pricing schemes in which switching among pricing is made by hierarchical fuzzy classification. Effectiveness of the proposed scheme is illustrated through experimental results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ghose, A., Yang, S.: An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets (working paper). In: SSRN (2009)

    Google Scholar 

  2. Feldman, J., Muthukrishnan, S.: Algorithmic Methods for Sponsored Search Advertising, Tutorial. In: SIGMETRICS (2008)

    Google Scholar 

  3. Spink, A., Jansen Bernard, J.: Commerce Related Web Search: Current Trends. In: Proceedings 18th Australasian Conference on Information Systems (ACIS 2007), pp. 1–6 (2007)

    Google Scholar 

  4. Balcan, M., Blum, A., Hartline, J.D., Mansour, Y.: Sponsored Search Auction Design via Machine Learning. In: Workshop on Sponsored Search Auctions, EC 2005 (2005)

    Google Scholar 

  5. Aggarwal, G., Feldman, J., Muthukrishnan, S., Pal, M.: Sponsored search auctions with markovian users. In: Papadimitriou, C., Zhang, S. (eds.) WINE 2008. LNCS, vol. 5385, pp. 621–628. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  6. Robu, V., La Poutré, H., Bohte, S.: The Complex Dynamics of Sponsored Search Markets? In: Cao, L., Gorodetsky, V., Liu, J., Weiss, G., Yu, P.S. (eds.) ADMI 2009. LNCS, vol. 5680, pp. 183–198. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  7. Cheng, H., Erick, C.P., Personalized click prediction in sponsored search auctions. In: The Third ACM International Conference on Web Search and Data Mining, New York, USA, pp. 351–360 (2010)

    Google Scholar 

  8. Hillard, D., Schroedl, S., Manavoglu, E., Hema, R., Leggetter, C.: Improving Ad Relevance in Sponsored Search. In: The Third ACM International Conference on Web Search and Data Mining, New York, USA, pp. 361–370 (2010)

    Google Scholar 

  9. C5.0, Release 2.02 (September 2005), http://www.rulequest.com/see5-info.html

  10. Varian, H.: Position auctions. International Journal of Industrial Organization 25(6), 1163–1178 (2007)

    Article  Google Scholar 

  11. Ostrovsky, M., Edelman, B., Schwarz, M.: Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. American Economic Reviews 97(1), 242–249 (2006)

    Google Scholar 

  12. Madhu, K., Kamal, B.K.: Fuzzy Logic Based Effective Range Computation and Bidder’s Behavior Estimation in Keyword Auctions. In: IEEE 2nd International Advance Computing Conference Patiala, India, February 19-20 (2010)

    Google Scholar 

  13. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)

    Google Scholar 

  14. Nazerzadeh, H., Saberi, A., Vohra, R.: Dynamic cost-per-action mechanisms and applications to online advertising. In: WWW 2008, pp. 179–188 (2008)

    Google Scholar 

  15. Madhu, K., Kamal, B.K.: Revenue Estimation and Quantification in Sponsored Search Auctions: An Inductive Learning Approach. To appear in ICDEM. LNCS, vol. 6411. Springer, Heidelberg (2010)

    Google Scholar 

  16. Ashkan, A., Clarke, C., Agichtein, E., Guo, Q.: Characterizing query intent from sponsored search clickthrough data. In: SIGIR Workshop on Informational Retrieval for Advertising, pp. 15–22 (2008)

    Google Scholar 

  17. Ashish, G., Kamesh, M.: Hybrid keyword search auctions. In: WWW 2009, pp. 221–230 (2009)

    Google Scholar 

  18. Aggarwal, G., Goel, A., Motwani, R.: Truthful auctions for pricing search keywords. In: EC 2006: The 7th ACM Conference on Electronic Commerce, New York, NY, USA, pp. 1–7 (2006)

    Google Scholar 

  19. Klawonn, F., Kruse, R.: Derivation of fuzzy classification rules from multidimensional data. In: Advances in Intelligent Data Analysis, Windsor, Ontario, pp. 90–94 (1995)

    Google Scholar 

  20. Cox, E.: The Fuzzy Systems Handbook, 2nd edn. Academic Press Professional, London (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

    Madhu Kumari & Kamal K. Bharadwaj

Authors
  1. Madhu Kumari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamal K. Bharadwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Southeastern Louisiana University, 70402, Hammond, LA, USA

    David C. Wyld

  2. Chair of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michal Wozniak

  3. University of Calcutta, Calcutta, India

    Nabendu Chaki

  4. Jackson State University Jackson, 39217, MS, USA

    Natarajan Meghanathan

  5. Wireilla Net Solutions PTY Ltd, Melbourne, Victoria, Australia

    Dhinaharan Nagamalai

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kumari, M., Bharadwaj, K.K. (2011). An Adaptive Pricing Scheme in Sponsored Search Auction: A Hierarchical Fuzzy Classification Approach. In: Wyld, D.C., Wozniak, M., Chaki, N., Meghanathan, N., Nagamalai, D. (eds) Advances in Computing and Information Technology. ACITY 2011. Communications in Computer and Information Science, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22555-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-22555-0_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22554-3

  • Online ISBN: 978-3-642-22555-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation