Threshold Influence Model for Allocating Advertising Budgets
Atsushi Miyauchi, Yuni Iwamasa, Takuro Fukunaga, Naonori Kakimura
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.
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.}
}