This paper presents a novel mechanism design for multi-item auction settings with uncertain bidders' type distributions. Our proposed approach utilizes nonparametric density estimation to accurately estimate bidders' types from historical bids, and is built upon the Vickrey-Clarke-Groves (VCG) mechanism, ensuring satisfaction of Bayesian incentive compatibility (BIC) and $\delta$-individual rationality (IR). To further enhance the efficiency of our mechanism, we introduce two novel strategies for query reduction: a filtering method that screens potential winners' value regions within the confidence intervals generated by our estimated distribution, and a classification strategy that designates the lower bound of an interval as the estimated type when the length is below a threshold value. Simulation experiments conducted on both small-scale and large-scale data demonstrate that our mechanism consistently outperforms existing methods in terms of revenue maximization and query reduction, particularly in large-scale scenarios. This makes our proposed mechanism a highly desirable and effective option for sellers in the realm of multi-item auctions.

翻译：本文提出了一种面向多物品拍卖场景中投标人类型分布不确定性的新型机制设计。该方法利用非参数密度估计从历史投标数据中精确估计投标人类型，并以维克里-克拉克-格罗夫斯(VCG)机制为基础，确保满足贝叶斯激励相容(BIC)与$\delta$个体理性(IR)约束。为提升机制效率，我们引入两种创新查询缩减策略：通过过滤方法筛查基于估计分布生成的置信区间内潜在赢家的价值区域，以及采用分类策略将低于阈值的区间长度对应的下界指定为估计类型。在小型与大规模数据集上的仿真实验表明，我们的机制在收益最大化和查询缩减方面持续优于现有方法，特别是在大规模场景下表现突出。这使得所提机制成为多物品拍卖领域对卖家极具吸引力的高效解决方案。