Coverage Patterns-Based Approach to Allocate Advertisement Slots for Display Advertising

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9671)


Display advertising is one of the predominant modes of online advertising. A publisher makes efforts to allocate the available ad slots/page views to meet the demands of the maximum number of advertisers for maximizing the revenue. Investigating efficient approaches for ad slot allocation to advertisers is a research issue. In the literature, efforts are being made to propose approaches by extending optimization techniques. In this paper, we propose an improved approach for ad slot allocation by exploiting the notion of coverage patterns. In the literature, an approach is proposed to extract the knowledge of coverage patterns from the transactional databases. In the display advertising scenario, we propose an efficient ad slot allocation approach by exploiting the knowledge of coverage patterns extracted from the click stream transactions. The proposed allocation framework, in addition to the step of extraction of coverage patterns, contains mapping, ranking and allocation steps. The experimental results on both synthetic and real world click stream datasets show that the proposed approach could meet the demands of increased number of advertisers and reduces the boredom faced by user by reducing the repeated display of advertisements.


Internet monetization Computational advertising Display advertising Coverage patterns 


  1. 1.
    Frequent itemset mining implementations repository.
  2. 2.
    Double click (2015).
  3. 3.
    Interactive advertising bureau (2015).
  4. 4.
  5. 5.
  6. 6.
    Adler, M., Gibbons, P.B., Matias, Y.: Scheduling space-sharing for internet advertising. J. Sched. 5(2), 103–119 (2002)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Bharadwaj, V., Chen, P., Ma, W., Nagarajan, C., Tomlin, J., Vassilvitskii, S., Vee, E., Yang, J.: Shale: an efficient algorithm for allocation of guaranteed display advertising. In: The 18th International Conference on Knowledge Discovery and Data mining. pp. 1195–1203. ACM (2012)Google Scholar
  8. 8.
    Budhiraja, A., Reddy, P.K.: An approach to cover more advertisers in adwords. In: The 2nd International Conference on Data Science and Advanced Analytics. pp. 1–10. IEEE (2015)Google Scholar
  9. 9.
    Caruso, F., Giuffrida, G., Zarba, C.: Heuristic Bayesian targeting of banner advertising. J. Optim. Eng. 16(1), 247–257 (2015)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Chvatal, V.: A greedy heuristic for the set-covering problem. Math. Oper. Res. 4(3), 233–235 (1979)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Feige, U., Immorlica, N., Mirrokni, V., Nazerzadeh, H.: A combinatorial allocation mechanism with penalties for banner advertising. In: The 17th International Conference on World Wide Web. pp. 169–178. ACM (2008)Google Scholar
  12. 12.
    Feldman, J., Henzinger, M., Korula, N., Mirrokni, V.S., Stein, C.: Online stochastic packing applied to display ad allocation. In: Berg, M., Meyer, U. (eds.) ESA 2010, Part I. LNCS, vol. 6346, pp. 182–194. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Feldman, J., Mehta, A., Mirrokni, V., Muthukrishnan, S.: Online stochastic matching: beating 1–1/e. In: The 50th Annual Symposium on Foundations of Computer Science. pp. 117–126. IEEE (2009)Google Scholar
  14. 14.
    Garey, M.R., Johnson, D.S., Stockmeyer, L.: Some simplified NP-complete problems. In: The 6th Annual ACM Symposium on Theory of Computing. pp. 47–63. ACM (1974)Google Scholar
  15. 15.
    Ghosh, A., McAfee, P., Papineni, K., Vassilvitskii, S.: Bidding for representative allocations for display advertising. In: Leonardi, S. (ed.) Internet and Network Economics. LNCS, vol. 5929, pp. 208–219. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Han, J., Chang, C.C.: Data mining for web intelligence. Computer 35(11), 64–70 (2002)CrossRefGoogle Scholar
  17. 17.
    Hojjat, A., Turner, J., Cetintas, S., Yang, J.: Delivering guaranteed display ads under reach and frequency requirements. In: The 28th AAAI Conference on Artificial Intelligence. pp. 2278–2284. AAAI Press (2014)Google Scholar
  18. 18.
    Huang, e., Cercone, N., An, A.: Comparison of interestingness functions for learning web usage patterns. In: The 11th International Conference on Information and Knowledge Management. pp. 617–620. ACM (2002)Google Scholar
  19. 19.
    Mirrokni, V.S., Gharan, S.O., Zadimoghaddam, M.: Simultaneous approximations for adversarial and stochastic online budgeted allocation. In: The 23rd Annual Symposium on Discrete Algorithms. pp. 1690–1701. ACM-SIAM (2012)Google Scholar
  20. 20.
    Nakamura, A., Abe, N.: Improvements to the linear programming based scheduling of web advertisements. J. Electron. Commer. Res. 5(1), 75–98 (2005)CrossRefMATHGoogle Scholar
  21. 21.
    Srinivas, P.G., Reddy, P.K., Sripada, B., Kiran, R.U., Kumar, D.S.: Discovering coverage patterns for banner advertisement placement. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part II. LNCS, vol. 7302, pp. 133–144. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  22. 22.
    Srinivas, P.G., Reddy, P.K., Trinath, A.V., Sripada, B., Kiran, R.U.: Mining coverage patterns from transactional databases. J. Intell. Inf. Syst. 45(3), 423–439 (2015)CrossRefGoogle Scholar
  23. 23.
    Srinivas, P.G., Reddy, P.K., Trinath, A.V.: CPPG: efficient mining of coverage patterns using projected pattern growth technique. In: Li, J., Cao, L., Wang, C., Tan, K.C., Liu, B., Pei, J., Tseng, V.S. (eds.) PAKDD 2013 Workshops. LNCS, vol. 7867, pp. 319–329. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  24. 24.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.N.: Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explor. News Lett. 1(2), 12–23 (2000)CrossRefGoogle Scholar
  25. 25.
    Trinath, A., Gowtham Srinivas, P., Krishna Reddy, P.: Content specific coverage patterns for banner advertisement placement. In: The 1st International Conference on Data Science and Advanced Analytics. pp. 263–269. IEEE (2014)Google Scholar
  26. 26.
    Vee, E., Vassilvitskii, S., Shanmugasundaram, J.: Optimal online assignment with forecasts. In: The 11th Conference on Electronic Commerce. pp. 109–118. ACM (2010)Google Scholar
  27. 27.
    Yang, J., Vee, E., Vassilvitskii, S., Tomlin, J., Shanmugasundaram, J., Anastasakos, T., Kennedy, O.: Inventory allocation for online graphical display advertising. Computing Research Repository, CoRR (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Kohli Center on Intelligent Systems (KCIS) IIIT HyderabadGachibowliIndia

Personalised recommendations