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.

翻译：展示广告和广义分配问题是两个经过充分研究的在线打包问题，在广告分配及其他领域具有重要应用。在这两个问题中，广告流量在线到达并需立即分配给预算受限的广告主。已知存在能达到理想竞争比的最坏情况算法，但面对可预测且通常平稳的实际输入数据时，这类算法可能表现得过于保守。针对这一差异，我们为这两个问题开发了一种融合机器学习预测的算法，从而能在最坏情况基础上提升性能。该算法基于Feldman等人（2009）的研究工作，本质上与Mahdian等人（2007）的算法类似——后者首次为相关但结构更规整的广告词问题提出了学习增强型算法。我们采用创新性分析方法证明：该算法既能有效利用优质预测，又能对劣质预测保持鲁棒性。通过在合成数据和真实数据上对广泛预测场景的实验评估，我们的算法始终优于未使用预测的最坏情况算法。