Threshold influence model for allocating advertising budgets

Atsushi Miyauchi, Yuni Iwamasa, Takuro Fukunaga, Naonori Kakimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

We propose a new influence model for allocating budgets to advertising channels. Our model captures customer's sensitivity to advertisements as a threshold behavior; a customer is expected to be influenced if the influence he receives exceeds his threshold. Over the threshold model, we discuss two optimization problems. The first one is the budget-constrained influence maximization. We propose two greedy algorithms based on different strategies, and analyze the performance when the influence is submodular. We then introduce a new characteristic to measure the cost-effectiveness of a marketing campaign, that is, the proportion of the resulting influence to the cost spent. We design an almost linear-time approximation algorithm to maximize the cost-effectiveness. Furthermore, we design a better-approximation algorithm based on linear programming for a special case. We conduct thorough experiments to confirm that our algorithms outperform baseline algorithms.

Original languageEnglish
Title of host publication32nd International Conference on Machine Learning, ICML 2015
PublisherInternational Machine Learning Society (IMLS)
Pages1395-1404
Number of pages10
Volume2
ISBN (Electronic)9781510810587
Publication statusPublished - 2015
Externally publishedYes
Event32nd International Conference on Machine Learning, ICML 2015 - Lile, France
Duration: 2015 Jul 62015 Jul 11

Other

Other32nd International Conference on Machine Learning, ICML 2015
CountryFrance
CityLile
Period15/7/615/7/11

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Science Applications

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  • Cite this

    Miyauchi, A., Iwamasa, Y., Fukunaga, T., & Kakimura, N. (2015). Threshold influence model for allocating advertising budgets. In 32nd International Conference on Machine Learning, ICML 2015 (Vol. 2, pp. 1395-1404). International Machine Learning Society (IMLS).