Online Ad Allocation with Predictions

• URL: http://arxiv.org/abs/2302.01827v2
• Date: Wed, 24 May 2023 23:45:19 GMT
• Title: Online Ad Allocation with Predictions
• Authors: Fabian Spaeh, Alina Ene
• Abstract summary: We develop an algorithm for both problems that incorporate machine-learned predictions and can thus improve the performance beyond the worst-case. Our algorithm is consistently outperforming the worst-case algorithm without predictions.
• Score: 14.213973379473652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Display Ads and the generalized assignment problem are two well-studied online packing problems with important applications in ad allocation and other areas. In both problems, ad impressions arrive online and have to be allocated immediately to budget-constrained advertisers. Worst-case algorithms that achieve the ideal competitive ratio are known, but might act overly conservative given the predictable and usually tame nature of real-world input. Given this discrepancy, we develop an algorithm for both problems that incorporate machine-learned predictions and can thus improve the performance beyond the worst-case. Our algorithm is based on the work of Feldman et al. (2009) and similar in nature to Mahdian et al. (2007) who were the first to develop a learning-augmented algorithm for the related, but more structured Ad Words problem. We use a novel analysis to show that our algorithm is able to capitalize on a good prediction, while being robust against poor predictions. We experimentally evaluate our algorithm on synthetic and real-world data on a wide range of predictions. Our algorithm is consistently outperforming the worst-case algorithm without predictions.

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