Personalized Television Broadcasting: An Algorithmic Model for Organizing a Screen Stream
DOI:
https://doi.org/10.55544/ijrah.5.6.25Keywords:
personalized television broadcasting, algorithmic directing, adaptive screen flow, artificial intelligence, media curation, dynamic broadcasting schedule, algorithmic attention management, next-generation television, cognitive media, social networksAbstract
The aim of this article is to formulate the theoretical and practical foundations for the transition from a linear model of television broadcasting to a personalized one, based on algorithmic content management. In contrast to the traditional television schedule, where the sequence of programs is determined by the editorial board, personalized television is built on the principle of an adaptive screen flow, formed individually for each viewer on the basis of an analysis of their behavioral and psychographic data.
The article examines the concept of an algorithmic model for organizing the screen flow, in which artificial intelligence performs the functions of a curator, distributing and combining materials from various television channels and production companies depending on the dynamic profile of the user. The author proposes to consider this model not only as a technological innovation but also as a new form of directing — the directing of algorithms, where the organization of the viewer’s attention is carried out through a continuous process of prediction, adaptation, and feedback.
Personalized television is considered as the next stage in the evolution of screen media, where the boundaries between author, platform, and viewer become fluid, and television itself is transformed from broadcasting into a dialogic medium.
Prospects for further research are associated with the transition from a theoretical model to an applied project: on the basis of the presented concept, it is already possible to form an experimental platform of personalized broadcasting, where the parameters of algorithmic directing will be tested in practice. Such a project may include the development of a set of formal variables (rhythm, emotional intensity, tonality, degree of content novelty), the creation of a pilot database of television materials with multilevel annotation, as well as the modelling of a smart playout system that in real time constructs a flow of programs for a specific viewer. As a result, television is capable of approaching the operating principles of social networks, where content is selected dynamically on the basis of the interests and reactions of the audience, while preserving the directorial logic of constructing screen narration and editorial quality control of materials.
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References
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.
Agarwaal, A., Kanaujia, P., Sinha Roy, S., & Ghose, S. (2023). Robust and lightweight audio fingerprint for automatic content recognition. arXiv.
Amatriain, X., & Basilico, J. (2015). Recommender systems at Netflix. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook (pp. 385–419). Springer.
Bordwell, D., & Thompson, K. (2013). Film art: An introduction. McGraw-Hill Education.
Bucher, T. (2018). If...then: Algorithmic power and politics. Oxford University Press.
Couldry, N., & Hepp, A. (2017). The mediated construction of reality. Polity Press.
Davenport, T. H., & Beck, J. C. (2001). The attention economy: Understanding the new currency of business. Harvard Business School Press.
Flew, T. (2021). Understanding global media. Palgrave Macmillan.
Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. Boczkowski, & K. Foot (Eds.), Media technologies: Essays on communication, materiality, and society (pp. 167–194). MIT Press.
Jannach, D., Zanker, M., & Felfernig, A. (2018). Recommending based on implicit feedback. In B. Knijnenburg et al. (Eds.), Social information access (pp. 79–101). Springer.
Jenkins, H. (2006). Convergence culture: Where old and new media collide. New York University Press.
Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46–70.
Lobato, R., et al. (2024). Automated content recognition (ACR), smart TVs, and ad-tracking. SAGE Journals.
Lotz, A. D. (2014). The television will be revolutionized. New York University Press.
Manovich, L. (2013). Software takes command. Bloomsbury Academic.
Masthoff, J. (2011). Group recommender systems. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook (pp. 677–702). Springer.
McStay, A. (2018). Emotional AI: The rise of empathic media. Sage.
Napoli, P. M. (2011). Audience evolution: New technologies and the transformation of media audiences. Columbia University Press.
Oard, D. W., & Kim, J. (1998). Implicit feedback for recommender systems. University of Maryland.
Posner, M. I., & Rothbart, M. K. (2007). Research on attention networks as a model for the integration of psychological science. Annual Review of Psychology, 58, 1–23.
Reeves, B., & Lang, A. (2020). Neuroadaptive media: Using psychophysiology to improve human-media interaction. Media Psychology Review, 14(1).
Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people and places. Cambridge University Press.
Sundar, S. S., & Marathe, S. S. (2010). Personalization versus customization: The importance of agency, privacy, and power in personalized media environments. Human Communication Research, 36(3), 298–322.
Webster, J. G. (2014). The marketplace of attention: How audiences take shape in a digital age. MIT Press.
Wiener, N. (1948). Cybernetics: Or control and communication in the animal and the machine. MIT Press.
Zubair, M., & Gupta, A. (2021). Cognitive user modeling in adaptive multimedia systems. ACM Computing Surveys, 54(3), 1–32.
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