Predicting the number of bidders in public procurement

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

*المؤلف المقابل لهذا العمل

نتاج البحث

2 اقتباسات (Scopus)

ملخص

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.

اللغة الأصليةEnglish
عنوان منشور المضيف5th International Conference on Computer Science and Engineering, UBMK 2020
ناشرInstitute of Electrical and Electronics Engineers Inc.
الصفحات360-365
عدد الصفحات6
رقم المعيار الدولي للكتب (الإلكتروني)9781728175652
المعرِّفات الرقمية للأشياء
حالة النشرPublished - سبتمبر 2020
الحدث5th International Conference on Computer Science and Engineering, UBMK 2020 - Diyarbakir
المدة: سبتمبر ٩ ٢٠٢٠سبتمبر ١٠ ٢٠٢٠

سلسلة المنشورات

الاسم5th International Conference on Computer Science and Engineering, UBMK 2020

Conference

Conference5th International Conference on Computer Science and Engineering, UBMK 2020
الدولة/الإقليمTurkey
المدينةDiyarbakir
المدة٩/٩/٢٠٩/١٠/٢٠

ASJC Scopus subject areas

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بصمة

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