Designing auction parameters for online industrial auctions is a complex problem due to highly heterogeneous items. Currently, online auctioneers rely heavily on their experts in auction design. In this paper, we propose a data driven auction design framework that seamlessly combines prediction models and knowledge from experts into an optimization model. We show the proposed data driven approach improves upon the design from the experts for starting prices and display positions of items.

中文翻译：

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在线工业拍卖的数据驱动设计

由于高度异构的项目，为在线工业拍卖设计拍卖参数是一个复杂的问题。目前，在线拍卖师严重依赖他们的拍卖设计专家。在本文中，我们提出了一个数据驱动的拍卖设计框架，将预测模型和专家知识无缝结合到优化模型中。我们展示了提议的数据驱动方法改进了专家的设计，用于开始价格和显示项目的位置。