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We present a data-driven algorithm that advertisers can use to automate theirdigital ad-campaigns at online publishers. The algorithm enables the advertiserto search across available target audiences and ad-media to find the bestpossible combination for its campaign via online experimentation. The problemof finding the best audience-ad combination is complicated by a number ofdistinctive challenges, including (a) a need for active exploration to resolveprior uncertainty and to speed the search for profitable combinations, (b) manycombinations to choose from, giving rise to high-dimensional searchformulations, and (c) very low success probabilities, typically just a fractionof one percent. Our algorithm (designated LRDL, an acronym for LogisticRegression with Debiased Lasso) addresses these challenges by combining fourelements: a multiarmed bandit framework for active exploration; a Lasso penaltyfunction to handle high dimensionality; an inbuilt debiasing kernel thathandles the regularization bias induced by the Lasso; and a semi-parametricregression model for outcomes that promotes cross-learning across arms. Thealgorithm is implemented as a Thompson Sampler, and to the best of ourknowledge, it is the first that can practically address all of the challengesabove. Simulations with real and synthetic data show the method is effectiveand document its superior performance against several benchmarks from therecent high-dimensional bandit literature.

Advertising Media and Target Audience Optimization via High-dimensional Bandits