Budgeted online influence maximization

Pierre Perrault (1, 2, 3, 4) , Jennifer Healey (3) , Zheng Wen (5) , Michal Valko (5)

1 ENS Paris Saclay - Ecole Normale Supérieure Paris-Saclay
2 SEQUEL - Sequential Learning
3 Adobe Research
4 Scool - Scool
5 DeepMind [London]

We introduce a new budgeted framework for on-line influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a chosen influ-encer 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.

Domaines: Machine Learning [stat.ML]

Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko. Budgeted online influence maximization. International Conference on Machine Learning, 2020, Vienna, Austria. ⟨hal-02904278⟩