VEŠTAČKA INTELIGENCIJA I OTT PLATFORME – PREDNOSTI I RIZICI

Autori

DOI:

https://doi.org/10.59864/Oditor82601TC

Ključne reči:

OTT platforme, veštačka inteligencija (AI), Netflix, algoritmi, personalizacija sadržaja

Apstrakt

Digitalne OTT (Over-The-Top) platforme, koje korisnicima nude kataloge video i audio sadržaja, kao i strimovanje linearnih kanala, primenom alata veštačke inteligencije ušle su u novu fazu tržišnog takmičenja. Međutim, usled prekomerne automatizacije procesa distribucije, gledaoci često ostaju uskraćeni za sadržaje koje bi možda želeli da pogledaju, ali im algoritamski modeli to ne omogućavaju. U ovom radu analiziraju se pozitivni i negativni aspekti savremene tehnološke faze kroz razmatranje primene AI alata, etičkih standarda, pravne regulative i korisničkog iskustva. Za potrebe ispitivanja savremenih navika i percepcije korisnika OTT platformi sprovedeno je istraživanje putem onlajn anketnog upitnika. Rezultati su potvrdili polaznu pretpostavku da korisnici izražavaju bojazan u pogledu etičnosti primene AI tehnologija, kao i da sveprisutna personalizacija sadržaja nije uvek poželjna. S druge strane, analiza poslovnih praksi najpopularnije globalne platforme za video striming, Netflixa, potvrđuje da menadžment OTT platformi posmatra veštačku inteligenciju kao ključni alat za zadržavanje postojećih i privlačenje novih pretplatnika, sa ciljem očuvanja globalnog tržišnog položaja i povećanja prihoda.

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Reference

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

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2026-06-11

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