ADVANCED REAL-TIME AUDIENCE SEGMENTATION: A NOVEL APPROACH FOR STREAMING MEDIA SERVICES
DOI:
https://doi.org/10.34218/IJCET_16_01_273Keywords:
Audience Segmentation, Real-time Analytics, Emotional Response Analysis, Privacy-Preserving Architecture, Content PersonalizationAbstract
This article presents an innovative approach to audience segmentation in streaming media services. The system combines real-time behavioral analytics, contextual AI, and multi-modal data fusion to create dynamic, hyper-personalized audience profiles. Unlike traditional segmentation methods that rely on static demographic data and historical viewing patterns, this approach integrates real-time user interactions, emotional response analysis, and cross-platform data to predict viewer preferences and behaviors. The architecture leverages advanced technologies including Transformer-based models, federated learning, and edge computing to maintain user privacy while delivering personalized content recommendations. The implementation demonstrates significant improvements in user engagement, content discovery, and advertising effectiveness while maintaining strict privacy controls and system scalability.
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Copyright (c) 2025 Avinash Rahul Gudimetla (Author)
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