Personalization Evolution
Old Model
- • Flat metadata (genre, cast, director)
- • Simple ML recommendations
- • Passive suggestions
- • User must browse and decide
2026 Model
- • Emotional scene analysis
- • Multi-dimensional user profiles
- • Agentic orchestration
- • AI makes micro-decisions
Agentic Orchestration
Agentic AI doesn't just suggest — it acts:
🎯
Metadata triggers
AI adjusts recommendations based on real-time content metadata.
📺
Dynamic ad placement
AI decides when and which ads to show.
🔄
Real-time adaptation
System responds to viewing behavior instantly.
⚡
Micro-decisions
Hundreds of small choices made automatically.
Multi-Dimensional User Profiles
Modern personalization uses hundreds of real-time data points:
- Skip behavior — what content types users skip
- Search queries — what they're looking for
- Hover time — interest signals before clicking
- Trailer opt-outs — content that didn't appeal
- Time of day — viewing context
Business Results
Omdia Research
- +24% uplift in long-term retention
- ↓ reduced content discovery time
- ↑ increased viewing hours per session
EPG Service: The Metadata Foundation
AI personalization requires emotional and scene-level metadata. Without it, AI has a "data deficit".
Building AI personalization?
EPG Service provides emotional metadata, scene-level tags, and mood descriptors for next-gen AI personalization.
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