Budgeted Online Influence Maximization

Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7620-7631, 2020.

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 this Paper


BibTeX
@InProceedings{pmlr-v119-perrault20a, title = {Budgeted Online Influence Maximization}, author = {Perrault, Pierre and Healey, Jennifer and Wen, Zheng and Valko, Michal}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {7620--7631}, year = {2020}, editor = {Hal Daumé III and Aarti Singh}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/perrault20a/perrault20a.pdf}, url = { http://proceedings.mlr.press/v119/perrault20a.html }, 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.} }
Endnote
%0 Conference Paper %T Budgeted Online Influence Maximization %A Pierre Perrault %A Jennifer Healey %A Zheng Wen %A Michal Valko %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-perrault20a %I PMLR %P 7620--7631 %U http://proceedings.mlr.press/v119/perrault20a.html %V 119 %X 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.
APA
Perrault, P., Healey, J., Wen, Z. & Valko, M.. (2020). Budgeted Online Influence Maximization. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:7620-7631 Available from http://proceedings.mlr.press/v119/perrault20a.html .

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