用贝叶斯方法修正点击率和转化率估计，提升自动出价效率
Uncertainty Quantification of Click and Conversion Estimates for the Autobidding
- 基于贝叶斯思想，从噪声估计中恢复真实分布
- 在合成数据、iPinYou和BAT数据集上验证，显著提升出价性能
- 适合关注自动出价鲁棒性与广告投放优化的研究者
现代电商平台采用多种拍卖机制分配商品的付费展示位。为应对数百万次拍卖的规模挑战，平台依赖自动化出价算法推荐推广工具。这些算法通常依赖预训练机器学习模型提供的点击率（CTR）和转化率（CVR）估计。然而，此类模型预测存在不确定性，会显著影响自动化出价算法的表现。为此，我们提出DenoiseBid方法，通过修正生成的CTR和CVR估计，使最终出价在拍卖中更高效。该方法的核心思路是采用贝叶斯方法，将有噪声的CTR或CVR估计替换为从恢复分布中获得的值。为验证所提方法的性能，我们在合成数据、iPinYou和BAT数据集上进行了大量实验。为评估方法对噪声水平的鲁棒性，我们使用了合成噪声以及从预训练模型预测中估计的噪声。
原文摘要 · Abstract (English)
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.
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