Machine Learning Frameworks for Media Consumption Intelligence across OTT and Television Ecosystems

The rapid expansion of digital media ecosystems most prominently Over-The-Top (OTT) streaming platforms has fundamentally transformed audience behavior, content discovery paradigms, and consumption intelligence models. Traditional television ecosystems have relied for decades on panel-based audience measurement frameworks, but the growing ubiquity of connected devices, cloud-based distribution, and personalized recommendation engines necessitates the introduction of sophisticated machine learning (ML) approaches. Machine learning frameworks enable content providers, broadcasters, and advertisers to analyze heterogeneous data sources at scale—including viewership logs, device metadata, user demographic profiles, contextual signals, and multimodal content attributes—to construct unified intelligence layers for media consumption prediction and optimization. This paper presents a comprehensive examination of emerging ML-driven architectures for media consumption intelligence, emphasizing unified modeling across hybrid environments consisting of both legacy broadcast television and modern OTT ecosystems. Unlike traditional analytics approaches that treat linear TV and digital streaming as separate channels, the proposed frameworks integrate them under a cross-platform intelligence paradigm. This integration enables continuous measurement of content interaction, dynamic preference evolution, quality-of-experience (QoE) indicators, and personalized content affinity modeling. Furthermore, the increasing adoption of server-side ad insertion (SSAI), dynamic ad decisioning, and cross-device identity resolution makes ML essential for extracting actionable insights. We propose a modular ML framework that includes (1) a cross-platform data ingestion and alignment layer, (2) a multimodal feature-engineering pipeline incorporating metadata, textual descriptors, embeddings, and behavioral factors, (3) a multi-task learning (MTL) consumption-prediction module, (4) a graph-based recommendation and clustering layer, and (5) an explainability and policy-driven decisioning mechanism. This architecture is built with scalability, interoperability, and cloud-native deployment considerations, making it applicable to large-scale OTT platforms and traditional broadcast networks transitioning into digital convergence. The paper further elaborates methodological strategies including supervised learning for consumption prediction, unsupervised clustering for audience segmentation, reinforcement learning for personalized recommendations, and graph neural networks (GNNs) for cross-platform content affinity modeling. The challenges of handling sparse data, fragmented identity spaces, privacy constraints, and real-time inference latencies are also discussed. We evaluate the performance of these frameworks using simulated cross-platform datasets and demonstrate improvements in prediction accuracy, segmentation purity, and recommendation diversity. Our results indicate that unified ML frameworks significantly enhance the ability of media organizations to understand and adapt to evolving audience behaviors. By leveraging end-to-end automated pipelines, streaming providers and broadcasters can improve content scheduling, optimize ad placements, reduce churn, and strengthen long-term viewer engagement. Overall, this work contributes a holistic, scalable, and future-ready approach to media consumption intelligence across converging OTT and television ecosystems.