Budgeted Online Influence Maximization
Abstract
We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a chosen influencer set. Our approach models better the real-world setting where the cost of influencers varies and advertizers want to find the best value for their overall social advertising budget. We propose an algorithm assuming an independent cascade diffusion model and edge-level semi-bandit feedback, and provide both theoretical and experimental results. Our analysis is also valid for the cardinality-constraint setting and improves the state of the art regret bound in this case.
Cite
Text
Perrault et al. "Budgeted Online Influence Maximization." International Conference on Machine Learning, 2020.Markdown
[Perrault et al. "Budgeted Online Influence Maximization." International Conference on Machine Learning, 2020.](https://mlanthology.org/icml/2020/perrault2020icml-budgeted/)BibTeX
@inproceedings{perrault2020icml-budgeted,
title = {{Budgeted Online Influence Maximization}},
author = {Perrault, Pierre and Healey, Jennifer and Wen, Zheng and Valko, Michal},
booktitle = {International Conference on Machine Learning},
year = {2020},
pages = {7620-7631},
volume = {119},
url = {https://mlanthology.org/icml/2020/perrault2020icml-budgeted/}
}