In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be suboptimal due to their fixed allocation rules to optimize a single performance metric (e.g., revenue or social welfare). Recently, data-driven auctions, learned directly from auction outcomes to optimize multiple performance metrics, have attracted increasing research interests. However, the procedure of auction mechanisms involves various discrete calculation operations, making it challenging to be compatible with continuous optimization pipelines in machine learning. In this paper, we design \underline{D}eep \underline{N}eural \underline{A}uctions (DNAs) to enable end-to-end auction learning by proposing a differentiable model to relax the discrete sorting operation, a key component in auctions. We optimize the performance metrics by developing deep models to efficiently extract contexts from auctions, providing rich features for auction design. We further integrate the game theoretical conditions within the model design, to guarantee the stability of the auctions. DNAs have been successfully deployed in the e-commerce advertising system at Taobao. Experimental evaluation results on both large-scale data set as well as online A/B test demonstrated that DNAs significantly outperformed other mechanisms widely adopted in industry.

中文翻译：

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神经拍卖：电子商务广告拍卖机制的端到端学习

在电子商务广告中，共同考虑各种性能指标至关重要，例如用户体验、广告客户效用和平台收入。传统的拍卖机制，例如 GSP 和 VCG 拍卖，由于其固定的分配规则来优化单个绩效指标（例如收入或社会福利），因此可能不是最理想的。最近，直接从拍卖结果中学习以优化多个性能指标的数据驱动拍卖吸引了越来越多的研究兴趣。然而，拍卖机制的过程涉及各种离散计算操作，使得与机器学习中的连续优化管道兼容具有挑战性。在本文中，我们设计了 \underline{D}eep \underline{N}eural \underline{A}uctions (DNAs)，通过提出一种可微模型来放松离散排序操作，这是拍卖的一个关键组成部分，从而实现端到端的拍卖学习。我们通过开发深度模型来有效地从拍卖中提取上下文来优化性能指标，为拍卖设计提供丰富的功能。我们在模型设计中进一步整合了博弈理论条件，以保证拍卖的稳定性。DNAs已成功部署在淘宝电商广告系统中。大规模数据集和在线 A/B 测试的实验评估结果表明，DNA 的性能明显优于工业中广泛采用的其他机制。我们通过开发深度模型来有效地从拍卖中提取上下文来优化性能指标，为拍卖设计提供丰富的功能。我们在模型设计中进一步整合了博弈理论条件，以保证拍卖的稳定性。DNAs已成功部署在淘宝电商广告系统中。大规模数据集和在线 A/B 测试的实验评估结果表明，DNA 的性能明显优于工业中广泛采用的其他机制。我们通过开发深度模型来有效地从拍卖中提取上下文来优化性能指标，为拍卖设计提供丰富的功能。我们在模型设计中进一步整合了博弈理论条件，以保证拍卖的稳定性。DNAs已成功部署在淘宝电商广告系统中。大规模数据集和在线 A/B 测试的实验评估结果表明，DNA 的性能明显优于工业中广泛采用的其他机制。DNAs已成功部署在淘宝电商广告系统中。大规模数据集和在线 A/B 测试的实验评估结果表明，DNA 的性能明显优于工业中广泛采用的其他机制。DNAs已成功部署在淘宝电商广告系统中。大规模数据集和在线 A/B 测试的实验评估结果表明，DNA 的性能明显优于工业中广泛采用的其他机制。