Dealing with Interdependencies and Uncertainty in Multi-Channel Advertising Campaigns Optimization

Author:

Nuara Alessandro1,Sosio Nicola1,Trovà Francesco1,Zaccardi Maria Chiara1,Gatti Nicola1,Restelli Marcello1

Affiliation:

1. Politecnico di Milano, Italy

Publisher

ACM Press

Reference39 articles.

1. C. Alippi, M. Roveri, and F. Trovò. 2014. Learning causal dependencies to detect and diagnose faults in sensor networks. In SSCI. IEEE, 34-41.

2. C. Calderón and L. Liu. 2003. The direction of causality between financial development and economic growth. J DEV ECON 72, 1 (2003), 321-334.

3. D. A. Dickey and W. A. Fuller. 1979. Distribution of the estimators for autoregressive time series with a unit root. J AM STAT ASSOC 74, 366a (1979), 427-431.

4. I. M. Dinner, H. J. Van Heerde, and S. A. Neslin. 2014. Driving online and offline sales: The cross-channel effects of traditional, online display, and paid search advertising. J MARKETING RES 51, 5 (2014), 527-545.

5. Gabriele Farina and Nicola Gatti. 2017. Adopting the cascade model in ad auctions: Efficiency bounds and truthful algorithmic mechanisms. J ARTIF INTELL RES 59(2017), 265-310.

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