Threshold Influence Model for Allocating Advertising Budgets

Atsushi Miyauchi, Yuni Iwamasa, Takuro Fukunaga, Naonori Kakimura
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1395-1404, 2015.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v37-miyauchi15, title = {Threshold Influence Model for Allocating Advertising Budgets}, author = {Miyauchi, Atsushi and Iwamasa, Yuni and Fukunaga, Takuro and Kakimura, Naonori}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {1395--1404}, year = {2015}, editor = {Bach, Francis and Blei, David}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v37/miyauchi15.pdf}, url = {https://proceedings.mlr.press/v37/miyauchi15.html}, 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.} }
Endnote
%0 Conference Paper %T Threshold Influence Model for Allocating Advertising Budgets %A Atsushi Miyauchi %A Yuni Iwamasa %A Takuro Fukunaga %A Naonori Kakimura %B Proceedings of the 32nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Francis Bach %E David Blei %F pmlr-v37-miyauchi15 %I PMLR %P 1395--1404 %U https://proceedings.mlr.press/v37/miyauchi15.html %V 37 %X 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.
RIS
TY - CPAPER TI - Threshold Influence Model for Allocating Advertising Budgets AU - Atsushi Miyauchi AU - Yuni Iwamasa AU - Takuro Fukunaga AU - Naonori Kakimura BT - Proceedings of the 32nd International Conference on Machine Learning DA - 2015/06/01 ED - Francis Bach ED - David Blei ID - pmlr-v37-miyauchi15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 37 SP - 1395 EP - 1404 L1 - http://proceedings.mlr.press/v37/miyauchi15.pdf UR - https://proceedings.mlr.press/v37/miyauchi15.html AB - 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. ER -
APA
Miyauchi, A., Iwamasa, Y., Fukunaga, T. & Kakimura, N.. (2015). Threshold Influence Model for Allocating Advertising Budgets. Proceedings of the 32nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 37:1395-1404 Available from https://proceedings.mlr.press/v37/miyauchi15.html.

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