{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T22:18:59Z","timestamp":1778537939310,"version":"3.51.4"},"reference-count":90,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Plan of Science, Technology and Innovation of the Principality of Asturias","award":["FC-GRUPIN-IDI\/2018\/000225"],"award-info":[{"award-number":["FC-GRUPIN-IDI\/2018\/000225"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2020,11,25]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1155\/2020\/8858258","type":"journal-article","created":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T18:20:19Z","timestamp":1606328419000},"page":"1-20","source":"Crossref","is-referenced-by-count":20,"title":["Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4812-1621","authenticated-orcid":true,"given":"Manuel J.","family":"Garc\u00eda Rodr\u00edguez","sequence":"first","affiliation":[{"name":"Project Engineering Area, University of Oviedo, Oviedo 33012, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3217-8586","authenticated-orcid":true,"given":"Vicente","family":"Rodr\u00edguez Montequ\u00edn","sequence":"additional","affiliation":[{"name":"Project Engineering Area, University of Oviedo, Oviedo 33012, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Ortega Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Project Engineering Area, University of Oviedo, Oviedo 33012, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1411-6288","authenticated-orcid":true,"given":"Joaqu\u00edn M.","family":"Villanueva Balsera","sequence":"additional","affiliation":[{"name":"Project Engineering Area, University of Oviedo, Oviedo 33012, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","article-title":"Public procurement","author":"European Commission","year":"2017"},{"key":"2","article-title":"The economic impact of open data opportunities for value creation in europe","author":"E. 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