Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain
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,...
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| Veröffentlicht in: | Complexity (New York, N.Y.) N.Y.), 2020, Vol.2020 (2020), p.1-20 |
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| creator | Villanueva Balsera, Joaquín M. Ortega Fernández, Francisco Rodríguez Montequín, Vicente García Rodríguez, Manuel J. |
| description | 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. |
| doi_str_mv | 10.1155/2020/8858258 |
| format | Article |
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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.</description><identifier>ISSN: 1076-2787</identifier><identifier>EISSN: 1099-0526</identifier><identifier>DOI: 10.1155/2020/8858258</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Bids ; Business information ; Case studies ; Data analysis ; Data envelopment analysis ; Datasets ; Electronic government ; Government purchasing ; Literature reviews ; Machine learning ; Open data ; Procurement ; Suppliers ; Transparency</subject><ispartof>Complexity (New York, N.Y.), 2020, Vol.2020 (2020), p.1-20</ispartof><rights>Copyright © 2020 Manuel J. García Rodríguez et al.</rights><rights>Copyright © 2020 Manuel J. García Rodríguez et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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| source | DOAJ Directory of Open Access Journals; Wiley-Blackwell Open Access Titles; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Algorithms Bids Business information Case studies Data analysis Data envelopment analysis Datasets Electronic government Government purchasing Literature reviews Machine learning Open data Procurement Suppliers Transparency |
| title | Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain |
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