This paper explores the integration of strategic optimization methods in
search advertising, focusing on ad ranking and bidding mechanisms within
E-commerce platforms. By employing a combination of reinforcement learning and
evolutionary strategies, we propose a dynamic model that adjusts to varying
user interactions and optimizes the balance between advertiser cost, user
relevance, and platform revenue. Our results suggest significant improvements
in ad placement accuracy and cost efficiency, demonstrating the model's
applicability in real-world scenarios.

本文探讨了在电子商务平台中将战略优化方法引入搜索广告领域，重点关注广告排名和竞价机制。通过结合强化学习和进化策略，我们提出了一个动态模型，能够根据用户交互的变化优化广告成本、用户相关性和平台收益的平衡。我们的研究结果表明，在广告投放准确性和成本效率方面取得了显著的改进，证明了该模型在实际场景中的适用性。

优化搜索广告策略：将强化学习与广义第二价格拍卖相结合以实现增强型广告排名和出价

Optimizing Search Advertising Strategies: Integrating Reinforcement  Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and  Bidding

Accurately predicting the probabilities of user feedback, such as clicks and
conversions, is critical for ad ranking and bidding. However, there often exist
unwanted mismatches between predicted probabilities and true likelihoods due to
the shift of data distributions and intrinsic model biases. Calibration aims to
address this issue by post-processing model predictions, and field-aware
calibration can adjust model output on different feature field values to
satisfy fine-grained advertising demands. Unfortunately, the observed samples
corresponding to certain field values can be too limited to make confident
calibrations, which may yield bias amplification and online disturbance. In
this paper, we propose a confidence-aware multi-field calibration method, which
adaptively adjusts the calibration intensity based on the confidence levels
derived from sample statistics. It also utilizes multiple feature fields for
joint model calibration with awareness of their importance to mitigate the data
sparsity effect of a single field. Extensive offline and online experiments
show the superiority of our method in boosting advertising performance and
reducing prediction deviations.

我们提出了一种自信度感知的多字段校准方法，根据样本统计得到的自信度水平自适应调整校准强度，并利用多个特征字段进行联合模型校准，以减轻单一字段的数据稀疏效应，从而提升广告表现并降低预测误差。