ADVERTISING BIDDING OPTIMIZATION BY TARGETING BASED ON SELF-LEARNING DATABASE
Roman Kvуetnyy
rkvetny@sprava.netVinnytsia National Technical University (Ukraine)
https://orcid.org/0000-0002-9192-9258
Yuriy Bunyak
Spilna Sprava Company (Ukraine)
https://orcid.org/0000-0002-0862-880X
Olga Sofina
Vinnytsia National Technical University (Ukraine)
https://orcid.org/0000-0003-3774-9819
Oleksandr Kaduk
Vinnytsia National Technical University (Ukraine)
https://orcid.org/0009-0001-2388-9813
Orken Mamyrbayev
Institute of Information and Computational Technologies of the Kazakh National Technical University named after K. I. Satbayev (Kazakhstan)
https://orcid.org/0000-0001-8318-3794
Vladyslav Baklaiev
Taras Shevchenko National University of Kyiv (Ukraine)
https://orcid.org/0009-0008-5767-6964
Bakhyt Yeraliyeva
M. Kh. Dulaty Taraz Regional University (Kazakhstan)
https://orcid.org/0000-0002-8680-7694
Abstract
The method of targeting advertising on Internet sites based on a structured self-learning database is considered. The database accumulates data on previously accepted requests to display ads from a closed auction, data on participation in the auction and the results of displaying ads – the presence of a click and product installation. The base is structured by streams with features – site, place, price. Each such structural stream has statistical properties that are much simpler compared to the general ad impression stream, which makes it possible to predict the effectiveness of advertising. The selection of bidding requests only promising in terms of the result allows to reduce the cost of displaying advertising.
Keywords:
advertising bidding, targeting, targeted advertising, click predictionReferences
Adikari S., Dutta K.: Real Time Bidding in Online Digital Advertisement. New Horizons in Design Science 9073, 2015, 19–38.
DOI: https://doi.org/10.1007/978-3-319-18714-3_2
Google Scholar
Avila C. P., Vijaya M. S.. Click Through Rate Prediction for Display Advertisement. International Journal of Computer Applications 136(1), 2016, 18–24.
DOI: https://doi.org/10.5120/ijca2016908332
Google Scholar
Bisikalo O., Kharchenko V., Kovtun V., Krak I., Pavlov S.: Parameterization of the Stochastic Model for Evaluating Variable Small Data in the Shannon Entropy Basis. Entropy 2023, 25, 184 [http://doi.org/10.3390/e25020184].
DOI: https://doi.org/10.3390/e25020184
Google Scholar
Chapelle O.: Offline Evaluation of Response Prediction in Online Advertising Auctions. IW3C2, Florence, 2015, 943–944.
DOI: https://doi.org/10.1145/2740908.2742566
Google Scholar
Chapelle O., Manavoglu E., Rosales R.: Simple and scalable response prediction for display advertising. Transactions on Intelligent Systems and Technology (TIST) 5(4), 2015, Article No. 61, A1–A34.
DOI: https://doi.org/10.1145/2532128
Google Scholar
IAB 2014. OpenRTB API Specification Version 2.2. http://www.iab.net/media/file/
Google Scholar
Jahrer M., Töscher A., Lee J.-Y., Deng J., Zhang H., Spoelstra J.: Ensemble of collaborative filtering and feature engineered model for click through rate prediction. Proceedings of KDD Cup 2012 Workshop, Beijing 2012, 1222–1230.
Google Scholar
Juan Y., Zhuang Y., Chin W.-S., Lin C.-J.: Field-aware Factorization Machines for CTR Prediction. RecSys’16, Boston, 2016, 43–50.
DOI: https://doi.org/10.1145/2959100.2959134
Google Scholar
Kondakindi G., Rana S., Rajkumar A., Ponnekanti S. K., Parakh V.: A Logistic Regression Approach to Ad Click Prediction. Machine Learning Project, 2014, 399–400.
Google Scholar
McMahan H. B., Holt G., Sculley D., Young M., Ebner D., Grady J. et. al. Ad Click Prediction: A View from the Trenches. KDD’13, Chicago, 2013, 1222–1230.
DOI: https://doi.org/10.1145/2487575.2488200
Google Scholar
Nigam K. L., Afferty J., McCallum A.: Using maximum entropy for text classification. IJCAI-99 1, 1999, 61–67.
Google Scholar
Pan Z., Chen E., Liu Q., Xu T., Ma H., Lin H.: Sparse Factorization Machines for Click-through Rate Prediction. IEEE 16th International Conference on Data Mining, 2016, 400–409.
DOI: https://doi.org/10.1109/ICDM.2016.0051
Google Scholar
Richardson M., Dominowska E., Ragno R.: Predicting clicks: estimating the click-through rate for new ads. ACM, 2007, 521– 530.
DOI: https://doi.org/10.1145/1242572.1242643
Google Scholar
Sree Vani M.: Prediction of Mobile Ad Click Using Supervised Classification Algorithms. International Journal of Computer Science and Information Technologies 7 (2), 2016, 623–625.
Google Scholar
Ta A.-P.: Factorization Machines with Follow-The-Regularized-Leader for CTR prediction in Display Advertising. IEEE International Conference on Big Data, 2015, 2889–2891.
DOI: https://doi.org/10.1109/BigData.2015.7364112
Google Scholar
The Real-Time Bidding (RTB) Protocol specification, 2016 https://developers.google.com/ad-exchange/rtb
Google Scholar
Zhang W., Yuan S., Wang J.: Optimal Real-Time Bidding for Display Advertising. KDD’14, New York, 2014, 1097–1105.
DOI: https://doi.org/10.1145/2623330.2623633
Google Scholar
Authors
Roman Kvуetnyyrkvetny@sprava.net
Vinnytsia National Technical University Ukraine
https://orcid.org/0000-0002-9192-9258
Authors
Olga SofinaVinnytsia National Technical University Ukraine
https://orcid.org/0000-0003-3774-9819
Authors
Oleksandr KadukVinnytsia National Technical University Ukraine
https://orcid.org/0009-0001-2388-9813
Authors
Orken MamyrbayevInstitute of Information and Computational Technologies of the Kazakh National Technical University named after K. I. Satbayev Kazakhstan
https://orcid.org/0000-0001-8318-3794
Authors
Vladyslav BaklaievTaras Shevchenko National University of Kyiv Ukraine
https://orcid.org/0009-0008-5767-6964
Authors
Bakhyt YeraliyevaM. Kh. Dulaty Taraz Regional University Kazakhstan
https://orcid.org/0000-0002-8680-7694
Statistics
Abstract views: 995PDF downloads: 173
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Roman Kvуetnyy, Volodymyr Kotsiubynskyi, Serhii Husak, Yaroslav Movchan, Nataliia Dobrovolska, Sholpan Zhumagulova, Assel Aitkazina, Critical cybersecurity aspects for improving enterprise digital infrastructure protection , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 15 No. 1 (2025)
- Marko Andrushchenko, Karina Selivanova, Oleg Avrunin, Alla Kraievska, Orken Mamyrbayev, Kymbat Momynzhanova, Development of a mobile application for testing fine motor skills disorders , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 15 No. 1 (2025)