ADVERTISING BIDDING OPTIMIZATION BY TARGETING BASED ON SELF-LEARNING DATABASE

Roman Kvуetnyy

rkvetny@sprava.net
Vinnytsia 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 prediction

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

Download


Published
2023-12-20

Cited by

Kvуetnyy R., Bunyak, Y., Sofina, O., Kaduk, O., Mamyrbayev, O., Baklaiev, V., & Yeraliyeva, B. (2023). ADVERTISING BIDDING OPTIMIZATION BY TARGETING BASED ON SELF-LEARNING DATABASE. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(4), 66–72. https://doi.org/10.35784/iapgos.5376

Authors

Roman Kvуetnyy 
rkvetny@sprava.net
Vinnytsia National Technical University Ukraine
https://orcid.org/0000-0002-9192-9258

Authors

Yuriy Bunyak 

Spilna Sprava Company Ukraine
https://orcid.org/0000-0002-0862-880X

Authors

Olga Sofina 

Vinnytsia National Technical University Ukraine
https://orcid.org/0000-0003-3774-9819

Authors

Oleksandr Kaduk 

Vinnytsia National Technical University Ukraine
https://orcid.org/0009-0001-2388-9813

Authors

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

Authors

Vladyslav Baklaiev 

Taras Shevchenko National University of Kyiv Ukraine
https://orcid.org/0009-0008-5767-6964

Authors

Bakhyt Yeraliyeva 

M. Kh. Dulaty Taraz Regional University Kazakhstan
https://orcid.org/0000-0002-8680-7694

Statistics

Abstract views: 842
PDF downloads: 154


Most read articles by the same author(s)