Architectural Advancements for AI/ML-Driven TV Audience Analytics and Intelligent Viewership Characterization
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P113Keywords:
TV Audience Analytics, AI/ML-Driven Architecture, Intelligent Viewership Characterization, Streaming Data Analytics, Real-Time Analytics, Viewer Profiling, Content Recommendation, OTT PlatformsAbstract
The rapid proliferation of smart televisions, over-the-top (OTT) platforms, and multi-device media consumption has fundamentally transformed how television audiences are measured and understood. Traditional panel-based audience measurement systems struggle to capture the scale, granularity, and temporal dynamics of modern viewership behavior. This paper presents Architectural Advancements for AI/ML-Driven TV Audience Analytics and Intelligent Viewership Characterization, proposing scalable and intelligent system architecture tailored for data-intensive, real-time media ecosystems. The architecture integrates heterogeneous data sources including smart TVs, set-top boxes, streaming applications, and content metadata through high-throughput ingestion pipelines and fault-tolerant stream processing layers. Advanced data preprocessing, feature engineering, and sessionization mechanisms enable robust handling of noisy and high-velocity viewership data. At the intelligence layer, machine learning and deep learning models support viewer profiling, temporal viewing pattern analysis, content affinity learning, and predictive audience segmentation. Real-time analytics and low-latency inference pipelines facilitate adaptive content recommendation and personalized advertisement targeting, while batch analytics enable long-term trend analysis and strategic planning. The architecture further incorporates privacy-by-design principles, secure data transmission, and governance mechanisms to ensure regulatory compliance and ethical data usage. Experimental evaluation using real-world and simulated datasets derived from popular TV series demonstrates that AI/ML-driven models significantly outperform traditional audience measurement approaches in prediction accuracy and adaptability. Overall, this work highlights how modern architectural innovations enable intelligent, scalable, and future-ready TV audience analytics aligned with the evolving demands of contemporary media platforms
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