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МЕТОД ФОРМУВАННЯ КОНТЕКСТУ РЕКЛАМИ ТА ЦІЛЬОВОЇ АУДИТОРІЇ НА ОСНОВІ НАВЧАННЯ АСОЦІАТИВНИХ ПРАВИЛ
METHOD OF FORMING THE CONTEXT OF ADVERTISING AND TARGET AUDIENCE BASED ON ASSOCIATIVE RULES LEARNING

 Сторінки: 279-287. Номер: №5, 2022 (313)   
DOI: https://www.doi.org/10.31891/2307-5732-2022-313-5-279-287
Автори:
ЛІП’ЯНІНА-ГОНЧАРЕНКО Христина
Західноукраїнський національний університет
https://orcid.org/0000-0002-2441-6292
e-mail: xrustya.com@gmail.com
КОМАР Мирослав
Західноукраїнський національний університет
https://orcid.org/0000-0001-6541-0359
e-mail: mko@wunu.edu.ua
САЧЕНКО Анатолій
Західноукраїнський національний університет
https://orcid.org/0000-0002-0907-3682
e-mail: as@wunu.edu.ua
ЛЕНДЮК Тарас
Західноукраїнський національний університет
https://orcid.org/0000-0001-9484-8333
e-mail: tl@wunu.edu.ua

LIPIANINA-HONCHARENKO Khrystyna, KOMAR Myroslav,
SACHENKO Anatoliy, LENDIUK Taras
West Ukrainian National University

Анотація мовою оригіналу

Сьогодні важливими механізмами вивчення є контент та механізми його творення, проблеми впливу на цільову аудиторію, яка сама прагне формувати комунікаційні процеси. Інтернет-контент займає позиції потужної комунікаційної технології, яка продовжує стрімко розвиватися та набирати впливовості. Формування великої кількості рекламних оголошень, особливо текстів, обходиться надзвичайно дорого. Тому варто продумати, як можна генерувати ці тексти автоматично. У зв’язку з цим можна вважати, що розробка методу формування контексту реклами та цільової аудиторії на основі навчання асоціативних правил є актуальною та дає можливість підвищити результативність рекламних оголошень, а відповідно і зменшення затрат на інтернет-рекламу закладів вищої освіти. У якості вхідних даних використано опитування студентів спеціальності «Комп’ютерні науки», стосовно вступу. В опитуванні прийняли участь 152 студентів, дали відповідь на 10 питань. З результатів, методу формування контексту реклами та цільової аудиторії на основі навчання асоціативних правил, результативність оголошення в соціальних мережах, збільшилась що найменше на 23%, а ціна зменшилась на 90%.
Ключові слова: аналіз даних, рекламний вміст, вивчення асоціативних правил,, алгоритм апріорі, Фейсбук.

Розширена анотація англійською  мовою

Nowadays, important mechanisms of study are content and techniques of its creation, the problem of influencing the target audience, which itself seeks to shape communication processes. Internet content occupies a position of powerful communication technology, which continues to grow rapidly and gain influence. Creating a large number of advertisements, especially texts, is extremely expensive. Therefore, it is worth considering how generate these texts automatically. In this regard, it is possible to assume that the development of a method of forming the context of advertising and target audience based on learning associative rules is relevant and can increase the effectiveness of advertising, and thus reduce the cost of online advertising of higher education institutions. The input data used a survey of students majoring in Computer Science, regarding admission. The 152 students took part in the survey and answered 10 questions. The experimental results confirmed, the proposed method enabled to increase the effectiveness of advertising on social networks at least in 23%, and reduce the price in 90%.
Keywords: data analysis, advertising content, associative rules learning, apriori algorithm, Facebook

Література

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References

  1. Monastyrskaya M. M. Improving customer relationship management based on intelligent analysis of user behavior patterns / M. M. Monastyrskaya, V. I. Soloviev // Proceedings of the 2020 13th International Conference “Management of Large-Scale System Development” (MLSD), 2020. – P. 1-4. https://doi.org/10.1109/MLSD49919.2020.9247718.
  2. Griva A. Retail business analytics: Customer visit segmentation using market basket data / A. Griva, C. Bardaki, K. Pramatari, Katerina, D. Papakyriakopoulos // Expert Systems with Applications. – 2018. – Vol. 100. – P. 1-16. https://doi.org/10.1016/j.eswa.2018.01.029.
  3. Badriyah T. Recommendation system for property search using content based filtering method / T. Badriyah, S. Azvy, W. Yuwono, I. Syarif // Proceedings of the 2018 IEEE International Conference on Information and Communications Technology (ICOIACT), 2018. – P. 25–29. https://doi.org/10.1109/ICOIACT.2018.8350801.
  4. Shazad B. Finding temporal influential users in social media using association rule learning / B. Shazad, H. U. Khan, M. Farooq, A. Mahmood, I. Mehmood, S. Rho, Y. Nam // Intelligent Automation and Soft Computing. – 2020. – Vol. 26. – P. 87–98. https://doi.org/10.31209/2019.100000130.
  5. Vanaja S. Aspect-level sentiment analysis on e-commerce data / S. Vanaja, M. Belwal // Proceedings of the 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 2018. – P. 1275-1279. https://doi.org/10.1109/ICIRCA.2018.8597286.
  6. Agrawal R. Fast algorithms for mining association rules / R. Agrawal, R. Srikant // Proceedings of the 20th International Conference on Very Large Data Bases VLDB, September 1994. – Vol. 1215. – P. 487-499.
  7. Lipyanina H. Targeting model of HEI video marketing based on classification tree / H. Lipyanina, S. Sachenko, T. Lendyuk, A. Sachenko // Proceedings of the 16th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, ICTERI 2020, Kharkiv, Ukraine, 6-10 October 2020. – CEUR Workshop Proceedings. – Vol. 2732. – P. 487-498. http://ceur-ws.org/Vol-2732/20200487.pdf.
  8. Lipyanina H. Decision tree based targeting model of customer interaction with business page / H. Lipyanina, A. Sachenko, T. Lendyuk, S. Nadvynychny, S. Grodskyi // Proceedings of the third International Workshop on Computer Modeling and Intelligent Systems (CMIS-2020), April 27 – May 1, 2020. – CEUR Workshop Proceedings. – Vol. 2608. – P. 1001–1012. http://ceur-ws.org/Vol-2608/paper75.pdf.
  9. Wang F. Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns / F. Wang. K. Li. N. Duić. Z. Mi. B.-M. Hodge. M. Shafie-khah. J. P. S. Catalão // Energy Conversion and Management. – 2018. – Vol. 171. – P. 839–854. https://doi.org/10.1016/j.enconman.2018.06.017.
  10. AlZu’bi S. A novel recommender system based on apriori algorithm for requirements engineering / S. AlZu’bi, B. Hawashin, M. EIBes and M. Al-Ayyoub // Proceedings of the 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS), 2018. – P. 323-327. https://doi.org/10.1109/SNAMS.2018.8554909.
  11. Aguilar J. An adaptive intelligent management system of advertising for social networks: A case study of Facebook / J. Aguilar, G. Garcia // IEEE Transactions on Computational Social Systems. – 2018. – Vol. 5. – P. 20-32. https://doi.org/10.1109/TCSS.2017.2759188.
  12. Arasu B. S. A machine learning-based approach to enhancing social media marketing / B. S. Arasu, B. J. B. Seelan, N. Thamaraiselvan // Computers & Electrical Engineering. – 2020. – Vol. 86. – Article no. 106723. https://doi.org/10.1016/j.compeleceng.2020.106723.
  13. Popov A. Adaptive look-alike targeting in social networks advertising / A. Popov, D. Iakovleva // Procedia Computer Science. – 2018. – Vol. 136. – P. 255-264. https://doi.org/10.1016/j.procs.2018.08.264.
  14. Shah N. Research trends on the usage of machine learning and artificial intelligence in advertising / N. Shah, S. Engineer, N. Bhagat, et al. // Augment Hum Res. – 2020. – Vol. 5. – Article no. 19. https://doi.org/10.1007/s41133-020-00038-8.
  15. Liu Z. Development of advertising art design based on information technology / Z. Liu // in: J. Jansen B., Liang H., Ye J. (eds) International Conference on Cognitive based Information Processing and Applications (CIPA 2021), volume 85 of Lecture Notes on Data Engineering and Communications Technologies, Springer, Singapore, 2021. – P. 3-10. https://doi.org/10.1007/978-981-16-5854-9_1.
  16. Barabash O. Information technology of targeting: optimization of decision making process in a competitive environment / O. Barabash, G. Shevchenko, N. Dakhno, O. Neshcheret, A. Musienko // International Journal of Intelligent Systems and Applications. – 2017. – Vol. 9. – P. 1-9. https://doi.org/10.5815/ijisa.2017.12.01.
  17. Saito R. Analysis of fashion market trend using advertising data of shopping information site / R. Saito, K. Otake, T. Namatame // in: Meiselwitz G. (eds) Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing. HCII 2020, volume 12195 of Lecture Notes in Computer Science, Springer, Cham, 2020. – P. 389-400. https://doi.org/10.1007/978-3-030-49576-3_28.
  18. Wakimoto K. Keyword-based text generation for internet advertisement / K. Wakimoto, S. Kawamoto, P. Zhang // Proceedings of the 34th Annual Conference of the Japanese Society for Artificial Intelligence, 2020. – P. 1-4.
  19. Lee D. Advertising content and consumer engagement on social media: Evidence from Facebook / D. Lee, K. Hosanagar, H. S. Nair // Management Science. – 2018. – Vol. 64. – P. 5105-5131. https://doi.org/10.1287/mnsc.2017.2902.
  20. Jamison A. M. Vaccine-related advertising in the Facebook Ad Archive / A. M. Jamison, D. A. Broniatowski, M. Dredze, Z. Wood-Doughty, D. A. Khan, S. C. Quinn // Vaccine. – 2019. – Vol. 38. – P. 512-520. https://doi.org/10.1016/j.vaccine.2019.10.066.
  21. Youn S. Understanding ad avoidance on Facebook: Antecedents and outcomes of psychological reactance / S. Youn, S. Kim // Computers in Human Behavior. – 2019. – Vol. 98. – P. 232-244. https://doi.org/10.1016/j.chb.2019.04.025.
  22. White C. L. Social media ethics in the data economy: Issues of social responsibility for using Facebook for public relations / C. L. White, B. Boatwright // Public Relations Review. – 2020. – Vol. 46. – Article no. 101980. https://doi.org/10.1016/j.pubrev.2020.101980.
  23. Gitomer A. Geographic impressions in Facebook political ads / A. Gitomer, P. V. Oleinikov, L. M. Baum, et al., // Appl Netw Sci. – 2021. – Vol. 6. – Article no. 18. https://doi.org/10.1007/s41109-020-00350-7.

 

Post Author: Горященко Сергій

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