### 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 language | English |
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Title of host publication | 32nd International Conference on Machine Learning, ICML 2015 |

Publisher | International Machine Learning Society (IMLS) |

Pages | 1395-1404 |

Number of pages | 10 |

Volume | 2 |

ISBN (Electronic) | 9781510810587 |

Publication status | Published - 2015 |

Externally published | Yes |

Event | 32nd International Conference on Machine Learning, ICML 2015 - Lile, France Duration: 2015 Jul 6 → 2015 Jul 11 |

### Other

Other | 32nd International Conference on Machine Learning, ICML 2015 |
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Country | France |

City | Lile |

Period | 15/7/6 → 15/7/11 |

### Fingerprint

### ASJC Scopus subject areas

- Human-Computer Interaction
- Computer Science Applications

### Cite this

*32nd International Conference on Machine Learning, ICML 2015*(Vol. 2, pp. 1395-1404). International Machine Learning Society (IMLS).

**Threshold influence model for allocating advertising budgets.** / Miyauchi, Atsushi; Iwamasa, Yuni; Fukunaga, Takuro; Kakimura, Naonori.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*32nd International Conference on Machine Learning, ICML 2015.*vol. 2, International Machine Learning Society (IMLS), pp. 1395-1404, 32nd International Conference on Machine Learning, ICML 2015, Lile, France, 15/7/6.

}

TY - GEN

T1 - Threshold influence model for allocating advertising budgets

AU - Miyauchi, Atsushi

AU - Iwamasa, Yuni

AU - Fukunaga, Takuro

AU - Kakimura, Naonori

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

VL - 2

SP - 1395

EP - 1404

BT - 32nd International Conference on Machine Learning, ICML 2015

PB - International Machine Learning Society (IMLS)

ER -