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Machine Learning and Data Privacy in Digital Advertising

Year 2022, Volume: 15 Issue: 3, 1455 - 1474, 26.09.2022
https://doi.org/10.35674/kent.1145325

Abstract

Digital advertising provides great advantages such as lower advertising costs, fast and reliable feedbacks from customers, increased efficiency, and the ability to create detailed databases of customers, which make it increasingly more important for companies. Production of contents is mainly based on intuition and experience in conventional advertising, while it is based on data in digital advertising. This makes it possible to offer targeted advertisements that are customized according to the digital trails of consumers. Targeted advertising has become the focus of digital advertising, and methods that have been developed in this field open new horizons both for companies and researchers. To provide targeted advertisements for digital advertising, bidding machines or pricing engines that offer customized prices and promotions are typically generated by means of a machine learning algorithm. Machine learning provides companies with more power to control advertisements; but the most important issue of debate is the customization of advertisements and therefore the possibility that data privacy is compromised. This paper discusses the issue with a holistic approach by focusing on the concerns of data privacy in addition to the benefits of targeted advertisements and machine learning algorithms for businesses. This paper also discusses the steps that would prevent consumers from not proceeding with a purchase due to concerns about data privacy, while maintaining the high level of profitability gained thanks to targeted advertisements. As a result, the importance of using consumer data in digital advertising was emphasized. However, privacy should be configured within the limits of consumer privacy by making personal data privacy settings with machine learning algorithms. Thus, it will be possible for companies both to protect their profitability and prevent consumer losses due to data privacy.

References

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Dijital Reklamcılıkta Makine Öğrenmesi ve Veri Gizliliği

Year 2022, Volume: 15 Issue: 3, 1455 - 1474, 26.09.2022
https://doi.org/10.35674/kent.1145325

Abstract

Dijital reklamcılık düşük reklam maliyetleri, hızlı ve etkili tüketici geri bildirimi, artan verimlilik ve ayrıntılı müşteri tabanı oluşturma avantajlarından dolayı şirketler için giderek daha önemli hale gelmektedir. Geleneksel reklamcılıkta daha çok sezgiye ve tecrübeye dayanan içerik üretme, dijital reklamcılıkta veriye dayalıdır. Böylece tüketicilerin dijital izlerine göre kişiselleştirilmiş hedef reklamlar sunulmaktadır. Hedef reklamcılık, dijital reklamcılığın odağına yerleşirken, bu alanda geliştirilen yöntemler hem şirketler hem de araştırmacılar için yeni ufuklar açmaktadır. Dijital reklamcılıkta hedefli reklamların sunulmasında teklif verme makineleri veya kişiye özel fiyat ve promosyon sunan fiyatlandırma motoru, genel olarak gelişmiş bir makine öğrenmesi algoritmasıyla gerçekleştirilmektedir. Makine öğrenmesi, şirketlere reklam üzerinde daha fazla kontrol gücü verirken, en önemli tartışma konusu ise reklamların kişiselleştirilmesi ve bunun sonucu olarak veri gizliliği ihlallerinin yaşanabilmesidir. Bu makale, makine öğrenmesi algoritmaları ile hedef reklamcılığın işletmelere sağladığı faydalar yanında, veri gizliliği endişelerine de odaklanarak konuyu bütüncül bir yaklaşımla ele almaktadır. Makalede hedef reklamcılığın getirdiği yüksek karlılığı korurken, tüketicilerin veri gizliliği endişesiyle satın alma davranışından vazgeçmelerini engelleyecek adımların neler olduğu tartışılmıştır. Sonuç olarak tüketici verilerinin dijital reklamcılıkta kullanılmasının önemi ortaya çıkmıştır. Bununla birlikte makine öğrenmesi algoritmaları ile kişiye özgü veri gizlilik ayarlarının yapılarak mahremiyetin, tüketicinin gizlilik sınırları çerçevesinde yapılandırılması gerektiği vurgulanmaktadır. Böylece şirketlerin hem kârlılığı koruması hem de veri gizliliği nedeniyle tüketici kayıplarının önüne geçmesi mümkün olacaktır.

References

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  • Almuhimedi, H., Schaub, F., Sadeh, N., Adjerid, I., Acquisti, A., Gluck, J., ... ve Agarwal, Y. (2015, April). Your Location Has Been Shared 5,398 Times! A Field Study On Mobile App Privacy Nudging. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (ss.787-796).
  • Alzubi, O. A., Alzubi, J. A., Alweshah, M., Qiqieh, I., Al-Shami, S., ve Ramachandran, M. (2020). An Optimal Pruning Algorithm Of Classifier Ensembles: Dynamic Programming Approach. Neural Computing and Applications, 32(20), 16091-16107. https://doi.org/10.1007/s00521-020-04761-6
  • Avila Clemenshia, P. ve Vijaya, M. S. (2016). Click Through Rate Prediction For Display Advertisement. International Journal of Computer Applications (975-8887), 1(136), 18-24
  • Bansal, G., Zahedi, F.M. ve Gefen, D. (2010). The Impact Of Personal Dispositions On Information Sensitivity, Privacy Concern And Trust In Disclosing Health Information Online. Decision Support Systems, 49(2), 138-150. https://doi.org/10.1016/j.dss.2010.01.010
  • Bari, L., & O’Neill, D. P. (2019). Rethinking Patient Data Privacy in The Era Of Digital Health. Health Affairs, 12. https://www.healthaffairs.org/do/10.1377/forefront.20191210.216658
  • Baruh, L., Secinti, E. ve Cemalcilar, Z. (2017). Online Privacy Concerns And Privacy Management: A Meta-Analytical Review. Journal of Communication, 67(1), 26-53. https://doi.org/10.1111/jcom.12276
  • Bélanger, F. ve Crossler, R. E. (2011). Privacy In The Digital Age: A Review Of Information Privacy Research In Information Systems. MIS Quarterly, 1017-1041. https://doi.org/10.2307/41409971
  • Bélanger, F., Hiller, J. S. ve Smith, W. J. (2002). Trustworthiness In Electronic Commerce: The Role Of Privacy, Security, And Site Attributes. The Journal Of Strategic Information Systems, 11(3-4), 245-270. https://doi.org/10.1016/S0963-8687(02)00018-5
  • Bergemann, D. ve Bonatti, A. (2011). Targeting In Advertising Markets: Implications For Offline Versus Online Media. The RAND Journal of Economics, 42(3), 417-443. https://doi.org/10.1111/j.1756-2171.2011.00143.x
  • Bleier, A., Goldfarb, A. ve Tucker, C. (2020). Consumer Privacy And The Future Of Data-Based Innovation And Marketing. International Journal of Research in Marketing, 37(3), 466-480. https://doi.org/10.1016/j.ijresmar.2020.03.006
  • Breiman L. (2001). Random Forests, Machine Learning, 45 (1), 5-32.
  • Campbell, J., Goldfarb, A. ve Tucker, C. (2015). Privacy Regulation And Market Structure. Journal of Economics & Management Strategy, 24(1), 47-73. https://doi.org/10.1111/jems.12079
  • Chapelle, O., Manavoglu, E. ve Rosales, R. (2014). Simple And Scalable Response Prediction For Display Advertising. ACM Transactions on Intelligent Systems and Technology (TIST), 5(4), 1-34. https://doi.org/10.1145/2532128
  • Chen, S. ve Li, J. (2009, May). Factors Influencing The Consumers' Willingness To Buy In E-Commerce. In 2009 International Conference on E-Business and Information System Security (pp. 1-8). IEEE. https://doi.org/10.1109/EBISS.2009.5137979
  • Choi, J. A., & Lim, K. (2020). Identifying Machine Learning Techniques For Classification Of Target Advertising. ICT Express, 6(3), 175-180. https://doi.org/10.1016/j.icte.2020.04.012
  • Clarke, R. (1999). Internet Privacy Concerns Confirm The Case For Intervention. Communications of the ACM, 42(2), 60-67. https://doi.org/10.1145/293411.293475
  • Culnan, M. J. ve Armstrong, P. K. (1999). Information Privacy Concerns, Procedural Fairness, And Impersonal Trust: An Empirical Investigation. Organization Science, 10(1), 104-115. https://doi.org/10.1287/orsc.10.1.104
  • Dinev, T. ve Hart, P. (2006). An Extended Privacy Calculus Model For E-Commerce Transactions. Information Systems Research, 17(1), 61-80.
  • Drennan, J., Sullivan, G. ve Previte, J. (2006). Privacy, Risk Perception, And Expert Online Behavior: An Exploratory Study Of Household End Users. Journal of Organizational and End User Computing (JOEUC), 18(1), 1-22. https://doi.org/10.4018/joeuc.2006010101
  • Eastlick, M. A., Lotz, S. L. ve Warrington, P. (2006). Understanding Online B-To-C Relationships: An Integrated Model Of Privacy Concerns, Trust, And Commitment. Journal Of Business Research, 59(8), 877-886. https://doi.org/10.1016/j.jbusres.2006.02.006
  • Ekinci, E., Omurca, S. İ., Kırık, E. ve Taşçı, Ş. (2020). Tıp Veri Kümesi İçin Gizli Dirichlet Ayrımı. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 22 (64), 67-80. https://doi.org/10.21205/deufmd.2020226408
  • Goldfarb, A. ve Tucker, C. (2011). Online Display Advertising: Targeting And Obtrusiveness. Marketing Science, 30(3), 389-404. https://doi.org/10.1287/mksc.1100.0583
  • Gomez, J., Pinnick, T. ve Soltani, A. (2009). Knowprivacy: The Current State Of Web Privacy, Data Collection, And Information Sharing. Berkeley, CA: UC Berkeley School of Information. https://www.ischool.berkeley.edu/projects/2009/knowprivacy
  • Gülpınar Demirci, V. ve Altaş, D. (2020). Yapay sinir ağları. D. Altaş ve İ. E. Yıldırım (Ed.), Uygulamalı Çok Değişkenli İstatistik Teknikler İçinde (s.167-188). Eskişehir: Seçkin Yayınevi.
  • Gülpınar Demirci, V. ve Kaplan, B. (2020). Veri madenciliği ve pazarlama. C. Söylemez ve A. Kayabaşı (Ed.) Dijital Pazarlama: Güncel Konular içinde (s.253-282). Bursa: Ekin Yayınevi.
  • Hsu, C. W. ve Lin, C. J. (2002). A Comparison Of Methods For Multiclass Support Vector Machines. IEEE Transactions On Neural Networks, 13(2), 415-425. https://doi.org/10.1109/72.991427
  • Hummel, P., Braun, M. ve Dabrock, P. (2021). Own Data? Ethical Reflections On Data Ownership. Philosophy & Technology, 34(3), 545-572. https://doi.org/10.1007/s13347-020-00404-9
  • Johnson, G. A., Shriver, S. K. ve Du, S. (2020). Consumer Privacy Choice In Online Advertising: Who Opts Out And At What Cost To Industry?. Marketing Science, 39(1), 33-51. https://doi.org/10.1287/mksc.2019.1198
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There are 66 citations in total.

Details

Primary Language Turkish
Journal Section Review Article
Authors

Vildan Gülpınar Demirci 0000-0002-8824-5154

Publication Date September 26, 2022
Submission Date July 19, 2022
Published in Issue Year 2022 Volume: 15 Issue: 3

Cite

APA Gülpınar Demirci, V. (2022). Dijital Reklamcılıkta Makine Öğrenmesi ve Veri Gizliliği. Kent Akademisi, 15(3), 1455-1474. https://doi.org/10.35674/kent.1145325

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