Facebook Research Allocates Ad Funding (without AI)

Struck me because we did something similar and also quite different for advertising fund allocation very early on.    We the results of integer optimization models with montecarlo simulation, constrained to advertising agreements.   I like the fact that AI is never mentioned!  But in much later spins on this,  what could be called AI was used to check for accuracy and regulatory compliance to contractual agreements.  Nice.

Auto-placement of ad campaigns using multi-armed bandits  in Facebook Research

By: Vashist Avadhanula, Riccardo Colini Baldeschi, Stefano Leonardi, Karthik Abinav Sankararaman, Okke Schrijvers

What the research is:

We look at the problem of allocating the budget of an advertiser across multiple surfaces optimally when both the demand and the value are unknown. Consider an advertiser who uses the Facebook platform to advertise a product. They have a daily budget that they would like to spend on our platform. Advertisers want to reach users where they spend time, so they spread their budget over multiple platforms, like Facebook, Instagram, and others. They want an algorithm to help bid on their behalf on the different platforms and are increasingly relying on automation products to help them achieve it.

In this research, we model the problem of placement optimization as a stochastic bandit problem. In this problem, the algorithm is participating in k different auctions, one for each platform, and needs to decide the correct bid for each of the auctions. The algorithm is given a total budget B (e.g., the daily budget) and a time horizon T over which this budget should be spent. At each time-step, the algorithm should decide the bid it will associate with each of the k platform, which will be input into the auctions for the next set of requests on each of the platforms. At the end of a round (i.e., a sequence of requests), the algorithm sees the total reward it obtained (e.g., number of clicks) and the total budget that was consumed in the process, on each of the different platforms. Based on just this history, the algorithm should decide the next set of bid multipliers it needs to place. The goal of the algorithm is to maximize the total advertiser value with the given budget across the k platforms.   ... "    

Full paper:  https://arxiv.org/abs/2103.10246