Public tenders, complaints, machine learning and recommender systems: a case study in public administration

被引:1
|
作者
Nai, Roberto [1 ]
Meo, Rosa [1 ]
Morina, Gabriele [1 ]
Pasteris, Paolo [1 ]
机构
[1] Univ Turin, Comp Sci Dept, Corso Svizzera 185, I-10149 Turin, TO, Italy
关键词
Public procurement; Legal prediction; Complaint detection; Knowledge discovery; Natural language processing; Machine learning; Recommender system;
D O I
10.1016/j.clsr.2023.105887
中图分类号
D9 [法律]; DF [法律];
学科分类号
0301 ;
摘要
With the proliferation of e-procurement systems in the public sector, valuable and open information sources can be jointly accessed. Our research aims to explore different legal Open Data; in particular, we explored the data set of the National Anti-Corruption Authority in Italy on public procurement and the judges' sentences related to public procurement, published on the website of the Italian Administrative Justice from 2007 to 2022. Our first goal was to train machine learning models capable of automatically recognizing which procurement has led to disputes and consequently complaints to the Administrative Justice, identifying the relevant features of procurement that correspond to certain anomalies. Our second goal was to develop a recommender system on procurement to return similar procurement to a given one and find companies for bidders, depending on the procurement requirements.(c) 2023 Roberto Nai, Rosa Meo, Gabriele Morina, Paolo Pasteris. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
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页数:13
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