Predicting the number of bidders in public procurement

Mustafa Kaan Gorgun*, Mucahid Kutlu, Bedri Kamil Onur Tas

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Public procurement constitutes an important part of economical activities. In order to effectively use public resources, increasing competition among firms participating in public procurement is essential. In this work, we investigate the impact of content information on the number of bidders in public procurement. We explore 6 different groups of features including n-grams, named entities, language of notices, country of the authority, description length, and CPV codes. In our experiments, we show that our proposed models outperform all baselines. In particular, k-nearest neighbor model with n-grams achieves the best prediction accuracy. Our model can be used by public procurement officials to automatically examine procurement notices and detect the ones causing low competition. Besides, participating firms can use our model to predict potential competition they will face, and make better decisions accordingly.

Original languageEnglish
Title of host publication5th International Conference on Computer Science and Engineering, UBMK 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages360-365
Number of pages6
ISBN (Electronic)9781728175652
DOIs
Publication statusPublished - Sep 2020
Externally publishedYes
Event5th International Conference on Computer Science and Engineering, UBMK 2020 - Diyarbakir, Turkey
Duration: Sep 9 2020Sep 10 2020

Publication series

Name5th International Conference on Computer Science and Engineering, UBMK 2020

Conference

Conference5th International Conference on Computer Science and Engineering, UBMK 2020
Country/TerritoryTurkey
CityDiyarbakir
Period9/9/209/10/20

Keywords

  • Competitiveness Prediction
  • European Union
  • Public Procurement

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications

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