TY - GEN
T1 - Threshold influence model for allocating advertising budgets
AU - Miyauchi, Atsushi
AU - Iwamasa, Yuni
AU - Fukunaga, Takuro
AU - Kakimura, Naonori
N1 - Funding Information:
The authors thank the reviewers for useful comments and suggestions to improve the paper. Also, the authors thank Yusuke Kobayashi for fruitful discussion on this topic. AM is supported by a Grant-in-Aid for JSPS Fellows (No. 26-11908), TF is supported by a Grant-in-Aid for Young Scientists (B) (No. 25730008), and NK is supported by a Grant-in-Aid for Young Scientists (B) (No. 25730001). This work was supported by JST, ERATO, Kawarabayashi Large Graph Project.
Publisher Copyright:
Copyright © 2015 by the author(s).
PY - 2015
Y1 - 2015
N2 - 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.
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.
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M3 - Conference contribution
AN - SCOPUS:84969884767
T3 - 32nd International Conference on Machine Learning, ICML 2015
SP - 1395
EP - 1404
BT - 32nd International Conference on Machine Learning, ICML 2015
A2 - Blei, David
A2 - Bach, Francis
PB - International Machine Learning Society (IMLS)
T2 - 32nd International Conference on Machine Learning, ICML 2015
Y2 - 6 July 2015 through 11 July 2015
ER -