Modern e-commerce platforms employ various auction mechanisms to allocate paid slots for a given item. To scale this approach to the millions of auctions, the platforms suggest promotion tools based on the autobidding algorithms. These algorithms typically depend on the Click-Through-Rate (CTR) and Conversion-Rate (CVR) estimates provided by a pre-trained machine learning model. However, the predictions of such models are uncertain and can significantly affect the performance of the autobidding algorithm. To address this issue, we propose the DenoiseBid method, which corrects the generated CTRs and CVRs to make the resulting bids more efficient in auctions. The underlying idea of our method is to employ a Bayesian approach and replace noisy CTR or CVR estimates with those from recovered distributions. To demonstrate the performance of the proposed approach, we perform extensive experiments on the synthetic, iPinYou, and BAT datasets. To evaluate the robustness of our approach to the noise scale, we use synthetic noise and noise estimated from the predictions of the pre-trained machine learning model.

翻译：现代电子商务平台采用多种拍卖机制为给定商品分配付费广告位。为将这一方法扩展至数百万次拍卖，平台基于自动竞价算法提供推广工具。这些算法通常依赖于预训练机器学习模型提供的点击率（CTR）与转化率（CVR）估计值。然而，此类模型的预测具有不确定性，可能显著影响自动竞价算法的性能。为解决该问题，我们提出DenoiseBid方法，通过校正生成的CTR与CVR值，使竞价在拍卖中更具效率。本方法的核心思想是采用贝叶斯方法，用从恢复分布中获取的估计值替代含噪声的CTR或CVR估计。为验证所提方法的性能，我们在合成数据集、iPinYou数据集和BAT数据集上进行了广泛实验。为评估方法对噪声尺度的鲁棒性，我们使用了合成噪声以及从预训练机器学习模型预测中估计的噪声。