Electronic Commerce Research

, Volume 5, Issue 1, pp 75–98 | Cite as

Improvements to the Linear Programming Based Scheduling of Web Advertisements

  • Atsuyoshi Nakamura
  • Naoki Abe


We propose and evaluate a number of improvements to the linear programming formulation of web advertisement scheduling, which we have proposed elsewhere together with our colleagues [Langheinrich et al., 9]. In particular, we address a couple of important technical challenges having to do with the estimation of click-through rates and optimization of display probabilities (the exploration–exploitation trade-off and the issue of data sparseness and scalability), as well as practical aspects that are essential for successful deployment of this approach (the issues of multi-impressions and inventory management). We propose solutions to each of these issues, and assess their effectiveness by running large-scale simulation experiments.

banner advertisement scheduling linear programming exploration–exploitation trade-off inventory management 


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  1. [1]
    Abe, N. and A. Nakamura. (1999). “Learning to Optimally Schedule Internet Banner Advertisements.” In Proc. of the 16th International Conference on Machine Learning, pp. 12–21.Google Scholar
  2. [2]
    Bradley, G., G. Brown, and G. Graves. (1977). “Design and Implementation of a Large Scale Primal Trans-shipment Algorithm.” Management Science24, 1–34Google Scholar
  3. [3]
    Berry, D.A. and B. Fristedt. (1985). Bandit Problems. Chapman & Hall.Google Scholar
  4. [4]
    Chickering, D. and D. Heckerman. (2000). “Targeted Advertising with Inventory Management.” In Proc. of ACM Special Interest Group on E-Commerce (EC00), pp. 145–149.Google Scholar
  5. [5]
    Dantzig, G. (1963). Linear Programming and Extensions. Princeton University Press.Google Scholar
  6. [6]
    DoubleClick Inc. (2003). DoubleClick 2002 Full-Year Ad Serving Trends.Google Scholar
  7. [7]
    Feller, W. (1968). An Introduction to Probability Theory and its Applications, Vol. 1. Wiley, 3rd edition.Google Scholar
  8. [8]
    Gittins, J.C. (1988). Multi-Armed Bandit Allocation Indices. Chichester: Wiley.Google Scholar
  9. [9]
    Langheinrich, M., A. Nakamura, N. Abe, T. Kamba, and Y. Koseki. (1999). “Unintrusive Customization Techniques for Web Advertising.” Computer Networks31, 1259–1272.Google Scholar
  10. [10]
    Li, H. and N. Abe. (1998). “Word Clustering and Disambiguation Based on Co-Occurrence Data.” In Pro-ceedings of COLING-ACL, pp. 749–755.Google Scholar
  11. [11]
    Nakamura, A. (2002). “Improvements in Practical Aspects of Optimally Scheduling Web Advertising.” In Proc. of the 11th International World Wide Web Conference, pp. 536–541.Google Scholar
  12. [12]
    Nakamura, A. and N. Abe. (1998). “Collaborative Filtering Using Weighted Majority Prediction Algo-rithms.” In Proc. of the 15th International Conference on Machine Learning, pp. 395–403.Google Scholar
  13. [13]
    Pazzani, M. (1999). “A Framework for Collaborative, Content-Based and Demographic Filtering.” Artificial Intelligence Review13(5–6), 393–408.Google Scholar
  14. [14]
    Quinlan, R. (1993). C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.Google Scholar
  15. [15]
    Rissanen, J. (1978). “Modeling by Shortest Data Description.” Automatica14, 37–38.Google Scholar
  16. [16]
    Sakamoto, Y., Y. Ishiguro, and M. Kitagawa. (1986). Akaike Information Criterion Statistics. Dordrecht: Reidel.Google Scholar
  17. [17]
    Tomlin, J. (2000). “An Entropy Approach to Unintrusive Targeted Advertising on the Web.” Computer Networks33, 767–774.Google Scholar
  18. [18]
    Ye, Y. (1997). Interior Point Algorithms: Theory and Analysis, Wiley-Interscience Series in Discrete Mathematics and Optimization. New York: Wiley.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Atsuyoshi Nakamura
    • 1
  • Naoki Abe
    • 2
  1. 1.Division of Systems and Information Engineering, Graduate School of EngineeringHokkaido UniversitySapporoJapan
  2. 2.IBM Thomas J. Watson Research CenterYorktown HeightsUSA

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