Modelo Conceitual de Serviço de Recomendações para TV 3.0
Resumo
The evolution toward TV 3.0 establishes a hybrid broadcast–broadband ecosystem, application-oriented and characterized by new requirements for personalization and interoperability. Although recommender systems are widely explored on OTT platforms, their use in the television context still faces specific challenges, such as limited interaction, shared viewing, and constraints on data collection. This work proposes a conceptual model for a recommendation service based on multiple specialized agents coordinated by a central orchestrator. The proposal aims to provide a minimum viable abstraction, modular, extensible, and aligned with the constraints of the television environment. In addition to detailing the architecture, we present a hypothetical use case that illustrates the end-to-end flow and discuss implications for transparency, explainability, and governance of recommendations. The results are intended to support the adoption of personalization mechanisms in TV 3.0, reconciling technological innovation with requirements for reliability and usability.
Palavras-chave:
TV 3.0, serviços de recomendação, sistemas multiagentes
Referências
2024. ATSC A/300: ATSC 3.0 System. Technical Report. Advanced Television Systems Committee (ATSC). [link] Approved 17 July 2025.
Kushagra Agrawal and Nisharg Nargund. 2025. Neural Orchestration for Multi-Agent Systems: A Deep Learning Framework for Optimal Agent Selection in Multi-Domain Task Environments. arXiv preprint (2025). DOI: 10.48550/arXiv.2505.02861 arXiv:2505.02861 [cs.MA]
Iftikhar Alam, Shah Khusro, and Mumtaz Khan. 2019. Factors Affecting the Performance of Recommender Systems in a Smart TV Environment. Technologies 7, 2 (2019), 41. DOI: 10.3390/technologies7020041
Barilliance. 2024. Ecommerce Personalization Statistics: Personalized Recommendations Account for 31% of Revenue. [link] Estudo aponta que 31% da receita de e-commerce vem de recomendações personalizadas.
Brasil. 2023. Decreto nº 11.484, de 6 de abril de 2023: Dispõe sobre as diretrizes para a evolução do Sistema Brasileiro de Televisão Digital Terrestre (TV 3.0). Diário Oficial da União. [link]
Sylvia Chan-Olmsted, Lisa-Charlotte Wolter, and Elisa Dorothee Adam. 2020. Towards a Video consumer leaning spectrum: A medium-centric approach. Nordic Journal of Media Management 1, 2 (2020), 129–185.
Jiabao Fang, Shen Gao, Pengjie Ren, Xiuying Chen, Suzan Verberne, and Zhaochun Ren. 2024. A Multi-Agent Conversational Recommender System. arXiv preprint (2024). DOI: 10.48550/arXiv.2402.01135 arXiv:2402.01135 [cs.IR]
S. Geng, S. Liu, Z. Fu, C. Zhang, F. Li, et al. 2022. Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict (P5) Paradigm. arXiv preprint arXiv:2203.13366 (2022). [link]
Carlos A Gomez-Uribe and Neil Hunt. 2015. The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS) 6, 4 (2015), 1–19.
Xu Huang, Jianxun Lian, Yuxuan Lei, Jing Yao, Defu Lian, and Xing Xie. 2023. Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations. arXiv preprint (2023). DOI: 10.48550/arXiv.2308.16505 arXiv:2308.16505 [cs.IR]
Ministério das Comunicações. 2025. Televisão 3.0. [link] Página institucional.
Marcelo Moreno, Carlos Pernisa Júnior, Eduardo Barrere, Li-Chang Shuen, Carlos de Salles Soares Neto, Débora Christina Muchaluat Saade, Joel AF dos Santos, Sérgio Colcher, Daniel Moares, Derzu Omaia, et al. 2023. R&D progress on TV 3.0 application coding layer. SET INTERNATIONAL JOURNAL OF BROADCAST ENGINEERING 9 (2023).
Li-Chang Shuen, Paulo Borges, Iago Costa, Ingrid Carvalho, Carlos Soares Neto, and Marcelo Moreno. 2023. App-based TV 3.0 experience: Proposing a new viewer’s journey. In Anais Estendidos do XXIX Simpósio Brasileiro de Sistemas Multimídia e Web (Ribeirão Preto/SP). SBC, Porto Alegre, RS, Brasil, 131–135. DOI: 10.5753/webmedia_estendido.2023.236173
Eli Stein, Kelsey Robinson, Alexis Wolfer, Gaelyn Almeida, and Willow Huang. 2025. Unlocking the next frontier of personalized marketing. [link]
Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren. 2023. Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents. In Proceedings of EMNLP 2023. [link]
ZhefanWang, Yuanqing Yu,Wendi Zheng,Weizhi Ma, and Min Zhang. 2024. MACRec: a Multi-Agent Collaboration Framework for Recommendation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2760–2764. DOI: 10.1145/3626772.3657669 Also available as arXiv:2402.15235.
Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, and Enhong Chen. 2024. A Survey on Large Language Models for Recommendation. arXiv preprint (2024). DOI: 10.48550/arXiv.2305.19860 arXiv:2305.19860 [cs.IR]
Kushagra Agrawal and Nisharg Nargund. 2025. Neural Orchestration for Multi-Agent Systems: A Deep Learning Framework for Optimal Agent Selection in Multi-Domain Task Environments. arXiv preprint (2025). DOI: 10.48550/arXiv.2505.02861 arXiv:2505.02861 [cs.MA]
Iftikhar Alam, Shah Khusro, and Mumtaz Khan. 2019. Factors Affecting the Performance of Recommender Systems in a Smart TV Environment. Technologies 7, 2 (2019), 41. DOI: 10.3390/technologies7020041
Barilliance. 2024. Ecommerce Personalization Statistics: Personalized Recommendations Account for 31% of Revenue. [link] Estudo aponta que 31% da receita de e-commerce vem de recomendações personalizadas.
Brasil. 2023. Decreto nº 11.484, de 6 de abril de 2023: Dispõe sobre as diretrizes para a evolução do Sistema Brasileiro de Televisão Digital Terrestre (TV 3.0). Diário Oficial da União. [link]
Sylvia Chan-Olmsted, Lisa-Charlotte Wolter, and Elisa Dorothee Adam. 2020. Towards a Video consumer leaning spectrum: A medium-centric approach. Nordic Journal of Media Management 1, 2 (2020), 129–185.
Jiabao Fang, Shen Gao, Pengjie Ren, Xiuying Chen, Suzan Verberne, and Zhaochun Ren. 2024. A Multi-Agent Conversational Recommender System. arXiv preprint (2024). DOI: 10.48550/arXiv.2402.01135 arXiv:2402.01135 [cs.IR]
S. Geng, S. Liu, Z. Fu, C. Zhang, F. Li, et al. 2022. Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict (P5) Paradigm. arXiv preprint arXiv:2203.13366 (2022). [link]
Carlos A Gomez-Uribe and Neil Hunt. 2015. The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS) 6, 4 (2015), 1–19.
Xu Huang, Jianxun Lian, Yuxuan Lei, Jing Yao, Defu Lian, and Xing Xie. 2023. Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations. arXiv preprint (2023). DOI: 10.48550/arXiv.2308.16505 arXiv:2308.16505 [cs.IR]
Ministério das Comunicações. 2025. Televisão 3.0. [link] Página institucional.
Marcelo Moreno, Carlos Pernisa Júnior, Eduardo Barrere, Li-Chang Shuen, Carlos de Salles Soares Neto, Débora Christina Muchaluat Saade, Joel AF dos Santos, Sérgio Colcher, Daniel Moares, Derzu Omaia, et al. 2023. R&D progress on TV 3.0 application coding layer. SET INTERNATIONAL JOURNAL OF BROADCAST ENGINEERING 9 (2023).
Li-Chang Shuen, Paulo Borges, Iago Costa, Ingrid Carvalho, Carlos Soares Neto, and Marcelo Moreno. 2023. App-based TV 3.0 experience: Proposing a new viewer’s journey. In Anais Estendidos do XXIX Simpósio Brasileiro de Sistemas Multimídia e Web (Ribeirão Preto/SP). SBC, Porto Alegre, RS, Brasil, 131–135. DOI: 10.5753/webmedia_estendido.2023.236173
Eli Stein, Kelsey Robinson, Alexis Wolfer, Gaelyn Almeida, and Willow Huang. 2025. Unlocking the next frontier of personalized marketing. [link]
Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren. 2023. Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents. In Proceedings of EMNLP 2023. [link]
ZhefanWang, Yuanqing Yu,Wendi Zheng,Weizhi Ma, and Min Zhang. 2024. MACRec: a Multi-Agent Collaboration Framework for Recommendation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2760–2764. DOI: 10.1145/3626772.3657669 Also available as arXiv:2402.15235.
Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, and Enhong Chen. 2024. A Survey on Large Language Models for Recommendation. arXiv preprint (2024). DOI: 10.48550/arXiv.2305.19860 arXiv:2305.19860 [cs.IR]
Publicado
10/11/2025
Como Citar
POGGY, Arthur; BORGES, Paulo Victor; MORAES, Daniel de Sousa; COLCHER, Sérgio.
Modelo Conceitual de Serviço de Recomendações para TV 3.0. In: WORKSHOP FUTURO DA TV DIGITAL INTERATIVA - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 31. , 2025, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 259-264.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2025.16811.
