ARTIFICIAL INTELLIGENCE AND OTT PLATFORMS -ADVANTAGES AND RISKS
DOI:
https://doi.org/10.59864/Oditor82601TCKeywords:
OTT platforms, artificial intelligence (AI), Netflix, algorithms, content personalizationAbstract
Digital OTT (Over-The-Top) platforms, which provide users with catalogs of video and audio content, as well as streaming of linear channels, have entered a new phase of market competition through the application of artificial intelligence tools. However, due to the excessive automation of the distribution process, viewers are often deprived of content that they might otherwise choose to watch, as algorithmic models do not always enable such exposure. This paper analyses the positive and negative aspects of the contemporary technological phase through an examination of the application of AI tools, ethical standards, legal regulation, and user experience. For the purposes of examining contemporary habits and perceptions of users regarding OTT platforms, research was conducted using an online survey questionnaire. The results confirmed the initial assumption that users express concern in terms of the ethical application of AI technologies, and that the omnipresent personalization of content is not always desirable. On the other hand, an analysis of the business practices of Netflix, the most popular global video-streaming platform, confirms that the management of OTT platforms views artificial intelligence as a key tool for retaining existing subscribers and attracting new ones, with the aim of preserving a global market position, and to increase the revenue.
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References
Arshad, M., Onn, C. W., Ahmad, A., Mogwe, G. (2025). Big data analytics and AI as success factors for online video streaming platforms. Frontiers in Big Data, 8, 1513027. https://doi.org/10.3389/fdata.2025.1513027
Bošković, Vuk. (July 1th, 2014). Uspon Netflixa – revolucija u procepu između piraterije i kablovske TV. Available at: https://www.beforeafter.rs/drustvo/netflix/.
Chapman H.E, Abraham A. (2025) Because You Watched: How Do Streaming Services' Recommender Systems Influence Aesthetic Choice? Behav Sci (Basel).; 15(11):1544. https://doi.org/10.3390/bs15111544
Correa, N. K., Galvão, C., Santos, J. W., Del Pino, C., Pinto, E. P., Barbosa, C., Massmann, D., Mambrini, R., Galvão, L., Terem, E., & de Oliveira, N. (2023). Worldwide AI Ethics: A review of 200 guidelines and recommendations for AI governance. AI Robotics Ethics Society. https://doi.org/10.1016/j.patter.2023.100857
Ćitić, T. (2025). Young adults and viewing habits of video content. SCIENCE International Journal, 4(3), 75-80. https://doi.org/10.35120/sciencej0403074c
Deloitte. (March 25th, 2025). 2025 Digital Media Trends: Social platforms are becoming a dominant force in media and entertainment. Dostupno preko: https://www.deloitte.com/us/en/insights/industry/technology/digital-media-trends-consumption-habits-survey/2025.html
Singh, M., Sharma, K., Kaur, A., & Keshav, M. (2024). AI-enabled content curation in OTT platforms: Balancing personalisation and privacy. Futuristic Trends in Artificial Intelligence (IIP Series, Vol. 3, Book 12, Part 7, Ch. 1, pp. 213–228). https://doi.org/10.58532/V3BIAI12P7CH1
Gaines, B. (July, 14th 2025). OTT Statistics & Global Market Share 2025 (Data By Country). Available at: https://evoca.tv/ott-statistics
Gomez-Uribe, C. A., & Hunt, N. (2015). The Netflix Recommender System: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems, 6(4), 1–19. https://doi.org/10.1145/2843948
Gorringe, J. (2025, April 17). Netflix vs Amazon Prime Video: Which streaming platform is better? Trusted Reviews. Available at: https://www.trustedreviews.com/versus/netflix-vs-amazon-prime-video-2918310
Johnson, C. (2019). Online TV (1st ed.). Routledge. https://doi.org/10.4324/9781315396828
Klimashevskaia, A., Jannach, D., Elahi, M. et al. (2024). A survey on popularity bias in recommender systems. User Model User-Adap Inter 34, 1777–1834. https://doi.org/10.1007/s11257-024-09406-0.
Liu, L., De Costa, M. F. S., Muhammad, M. S., Gong, S., & Liu, B. (2024). The moderating effect of algorithm literacy on Over-The-Top platform adoption. Entertainment Computing, 49, 100623. https://doi.org/10.1016/j.entcom.2023.100623
Manfredi, L. (21th January 2025). Netflix Tops 300 Million Subscribers Globally, Adds Record-Breaking 19 Million in Q4. Available at: https://www.thewrap.com/netflix-earnings-q4-2024/
Milosaljević, I. (2025). How Netflix’s Recommendation Algorithms Function in Small Markets – The Case of Serbia. AM Journal of Art and Media Studies, (36). https://doi.org/10.25038/am.v0i28.612
Milosavljević, I., & Simeunović Bajić, N. (2025). The impact of Netflix and video streaming services on changes in distribution, production, and audience of television series. Društvene i humanističke studije, 10(1), 763–784. https://doi.org/10.51558/2490-3647.2025.10.1.763
Munson, J., Cummins, B. & Zosso, D. (2025) An introduction to collaborative filtering through the lens of the Netflix Prize. Knowl Inf Syst 67, 3049–3098. https://doi.org/10.1007/s10115-024-02315-z
Netflix. How Netflix's recommendation system works. Dostupno preko: How Netflix’s Recommendations System Works | Netflix Help Center
Pandey R., & Rashid Khan, MD. (2024). The behaviour of consumers towards subscriptions to online streaming applications. Academy of Marketing Studies Journal, 28(3), 1-19. Available at: https://www.abacademies.org/articles/the-behaviour-of-consumers-towards-subscriptions-to-online-streaming-applications.pdf.
Qazi, M. A., Nadeem, M., Sohail, S. S., Imam, R., Doctor, F., Himeur, Y., Hussain, A., & Amira, A. (2023). Filter bubbles in recommender systems: Fact or fallacy — A systematic review. arXiv:2307.01221. arXiv. https://doi.org/10.48550/arXiv.2307.01221
Računarski fakultet. Šta je algoritam. Available at: https://raf.edu.rs/citaliste/sta-je-algoritam/
Sorbán, K. (2021). Ethical and legal implications of using AI-powered recommendation systems in streaming services. Információs Társadalom XXI, no. 2 ): 63–82. https://doi.org/10.22503/inftars.XXI.2021.2.5
Wang, M., Hu, Y., Wu, S., Li, W., Bai, Q., & Rupar, V. (2024). Balancing information perception with Yin–Yang: Agent-based information neutrality model for recommendation systems. arXiv:2404.04906. https://doi.org/10.48550/arXiv.2404.04906
Yasmeen, K., & Hashmat, S. (2025). The role of AI-driven algorithms on user engagement and media consumption trends in OTT platforms. The Research of Medical Science Review, 3(6), 9–21. DOI: https://doi.org/10.5281/zenodo.15581215
Ding, Y., & Li, X. (2005). Time weight collaborative filtering. Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM ‘05) (pp. 485–492). Association for Computing Machinery. https://doi.org/10.1145/1099554.1099689
