TY - GEN
T1 - Predicting the number of bidders in public procurement
AU - Gorgun, Mustafa Kaan
AU - Kutlu, Mucahid
AU - Tas, Bedri Kamil Onur
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Competitiveness Prediction
KW - European Union
KW - Public Procurement
UR - http://www.scopus.com/inward/record.url?scp=85095685256&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85095685256&partnerID=8YFLogxK
U2 - 10.1109/UBMK50275.2020.9219404
DO - 10.1109/UBMK50275.2020.9219404
M3 - Conference contribution
AN - SCOPUS:85095685256
T3 - 5th International Conference on Computer Science and Engineering, UBMK 2020
SP - 360
EP - 365
BT - 5th International Conference on Computer Science and Engineering, UBMK 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Computer Science and Engineering, UBMK 2020
Y2 - 9 September 2020 through 10 September 2020
ER -