Optimal Advertisement Strategies for Small and Big Companies

  • Tossou AristideEmail author
  • Christos Dimitrakakis
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 171)


Many small and big companies in developing countries struggle to make their products or services known to the public. This is especially the case when there are new or have a new product. Most of them use publicity through radio, tv, social networks, billboard, SMS... Moreover, they also need to decide at what time to display their publicity for maximal effects. The companies which have more money typically used a simple strategy which consists in doing the publicity at many sources at different time or at a time such as to maximize the number of viewers. The smaller ones typically target the best popular programs.

However, this strategy is not the best as many users listening to your publicity might not be interested in it. So, you are more likely to miss the interested readers. Moreover, there will be many other competing publicities.

We propose a strategy by using the Multi-Armed bandit problem to optimally solve this problem under realistic assumptions. We further extend the model to deal with many competing companies by proposing the use of a time-division sharing algorithm.


Bandit algorithm Advertisement Developing countries 


  1. 1.
    Alon, N., Gamzu, I., Tennenholtz, M.: Optimizing budget allocation among channels and influencers. In: Proceedings of the 21st International Conference on World Wide Web, WWW 2012, pp. 381–388 (2012)Google Scholar
  2. 2.
    Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite time analysis of the multiarmed bandit problem. Mach. Learn. 47(2/3), 235–256 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E.: The nonstochastic multiarmed bandit problem. SIAM J. Comput. 32(1), 48–77 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Brier, G.W.: Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78(1), 1–3 (1950). 30 Sept 2015CrossRefGoogle Scholar
  5. 5.
    Lai, T.L., Robbins, H.: Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6(1), 4–22 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Liu, K., Zhao, Q.: Distributed learning in multi-armed bandit with multiple players. IEEE Trans. Signal Process. 58(11), 5667–5681 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Maehara, T., Yabe, A., Kawarabayashi, K.: Budget allocation problem with multiple advertisers: a game theoretic view. In: ICML 32, JMLR Proceedings, vol. 37, pp. 428–437. (2015)Google Scholar
  8. 8.
    Miyauchi, A., Iwamasa, Y., Fukunaga, T., Kakimura, N.: Threshold influence model for allocating advertising budgets. In: ICML 32, JMLR Proceedings, vol. 37, pp. 1395–1404. (2015)Google Scholar
  9. 9.
    Pandey, S., Olston, C.: Handling advertisements of unknown quality in search advertising. In: NIPS 20, pp. 1065–1072. MIT Press (2006)Google Scholar
  10. 10.
    Radanovic, G., Faltings, B.: A robust Bayesian truth serum for non-binary signals. In: AAAI 27, pp. 833–839 (2013)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  1. 1.Chalmers University of TechnologyGothenburgSweden

Personalised recommendations