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Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain
Complexity ( IF 1.7 ) Pub Date : 2020-11-25 , DOI: 10.1155/2020/8858258
Manuel J. García Rodríguez 1 , Vicente Rodríguez Montequín 1 , Francisco Ortega Fernández 1 , Joaquín M. Villanueva Balsera 1
Affiliation  

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%。
更新日期:2020-11-25
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