Recommending the identity of bidders in public procurement auctions (tenders) has a significant impact in many areas of public procurement, but it has not yet been studied in depth. A bidders recommender would be a very beneficial tool because a supplier (company) can search appropriate tenders and, vice versa, a public procurement agency can discover automatically unknown companies which are suitable for its tender. This paper develops a pioneering algorithm to recommend potential bidders using a machine learning method, particularly a random forest classifier. The bidders recommender is described theoretically, so it can be implemented or adapted to any particular situation. It has been successfully validated with a case study: an actual Spanish tender dataset (free public information) which has 102,087 tenders from 2014 to 2020 and a company dataset (nonfree public information) which has 1,353,213 Spanish companies. Quantitative, graphical, and statistical descriptions of both datasets are presented. The results of the case study were satisfactory: the winning bidding company is within the recommended companies group, from 24% to 38% of the tenders, according to different test conditions and scenarios.

中文翻译：

---

使用机器学习进行公共采购拍卖的竞标人推荐：数据分析，算法和来自西班牙的竞标案例研究

在公共采购拍卖（投标）中推荐投标人的身份在公共采购的许多领域都具有重大影响，但尚未对其进行深入研究。投标人推荐者将是一个非常有益的工具，因为供应商（公司）可以搜索适当的投标，反之亦然，公共采购机构可以自动发现适合其投标的未知公司。本文开发了一种开创性算法，可以使用机器学习方法（尤其是随机森林分类器）来推荐潜在投标人。理论上描述了投标人推荐者，因此可以将其实施或适应于任何特定情况。已通过案例研究成功验证：实际的西班牙招标数据集（免费的公共信息）有102个，2014年至2020年共进行了087次招标，公司数据集（非免费公共信息）包含1,353,213家西班牙公司。给出了两个数据集的定量，图形和统计描述。案例研究的结果令人满意：根据不同的测试条件和场景，中标公司在推荐公司组中，占投标的24％至38％。