We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, several papers have recently proposed machine learning (ML)-based preference elicitation algorithms that aim to elicit only the most important information from bidders. However, from a practical point of view, the main shortcoming of this prior work is that those designs elicit bidders' preferences via value queries (i.e., ``What is your value for the bundle $\{A,B\}$?''). In most real-world ICA domains, value queries are considered impractical, since they impose an unrealistically high cognitive burden on bidders, which is why they are not used in practice. In this paper, we address this shortcoming by designing an ML-powered combinatorial clock auction that elicits information from the bidders only via demand queries (i.e., ``At prices $p$, what is your most preferred bundle of items?''). We make two key technical contributions: First, we present a novel method for training an ML model on demand queries. Second, based on those trained ML models, we introduce an efficient method for determining the demand query with the highest clearing potential, for which we also provide a theoretical foundation. We experimentally evaluate our ML-based demand query mechanism in several spectrum auction domains and compare it against the most established real-world ICA: the combinatorial clock auction (CCA). Our mechanism significantly outperforms the CCA in terms of efficiency in all domains, it achieves higher efficiency in a significantly reduced number of rounds, and, using linear prices, it exhibits vastly higher clearing potential. Thus, with this paper we bridge the gap between research and practice and propose the first practical ML-powered ICA.

翻译：我们研究了迭代组合拍卖（ICAs）的设计。该领域的主要挑战在于，物品种类数量增加时，竞价组合空间呈指数级增长。为解决这一问题，近期多篇论文提出了基于机器学习（ML）的偏好 elicitation 算法，旨在仅从竞拍者处收集最关键的信息。然而，从实践角度来看，这些先前工作的主要缺陷在于其设计通过价值查询（例如：“你对组合$\{A,B\}$的估价是多少？”）来获取竞拍者的偏好。在大多数实际 ICA 场景中，价值查询被认为不切实际，因为这会为竞拍者带来过高且不现实的认知负担，因此未被实际采用。本文通过设计一种基于机器学习的组合时钟拍卖来弥补这一缺陷，该机制仅通过需求查询（例如：“在价格$p$下，你偏好的物品组合是什么？”）从竞拍者处收集信息。我们做出了两项关键技术贡献：第一，提出了一种在需求查询上训练机器学习模型的新方法；第二，基于这些训练好的 ML 模型，我们引入了一种高效的方法来确定具有最高出清潜力的需求查询，并为此提供了理论基础。我们在多个频谱拍卖领域中对基于 ML 的需求查询机制进行了实验评估，并将其与最成熟的实际 ICA——组合时钟拍卖（CCA）进行了比较。我们的机制在所有领域中的效率均显著优于 CCA，能在明显更少的轮次内实现更高的效率，并且在使用线性价格时展现出远超 CCA 的出清潜力。因此，本文弥合了研究与实际应用之间的差距，提出了首个实用的基于 ML 的 ICA。