Stochastic bandits for multi-platform budget optimization in online advertising

The Web Conference


We study the problem of an online advertising system that wants to optimally spend an advertiser’s given budget for a campaign across multiple platforms, without knowing the value for showing an ad to the users on those platforms. We model this challenging practical application as a Stochastic Bandits with Knapsacks problem over T rounds of bidding with the set of arms given by the set of distinct bidding m-tuples, where m is the number of platforms. We modify the algorithm proposed in Badanidiyuru et al., [11] to extend it to the case of multiple platforms to obtain an algorithm for both the discrete and continuous bid-spaces. Namely, for discrete bid spaces we give an algorithm with regret O(OPTmn/B + √mnOPT), where OPT is the performance of the optimal algorithm that knows the distributions. For continuous bid spaces the regret of our algorithm is Õ(m1/3 · min {B2/3 , (mT)2/3). When restricted to this special-case, this bound improves over Sankararaman and Slivkins [34] in the regime OPT << T, as is the case in the particular application at hand. Second, we show an Ω (√mOPT) lower bound for the discrete case and an Ω (m1/3 B2/3) lower bound for the continuous setting, almost matching the upper bounds. Finally, we use a real-world data set from a large internet online advertising company with multiple ad platforms and show that our algorithms outperform common benchmarks and satisfy the required properties warranted in the real-world application.

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