Media Audience Preference Engine
This AI solution analyzes viewing, reading, and interaction patterns to infer granular audience preferences across news, entertainment, and streaming platforms. It powers personalized recommendations, content tagging, and adaptive experiences that increase engagement, session length, and subscription retention while reducing content discovery friction.
The Problem
“Granular preference inference to power media recs, tagging, and adaptive experiences”
Organizations face these key challenges:
1
Users bounce quickly because homepages and feeds feel generic or repetitive
2
Content discovery is poor for long-tail items and new releases (cold start)
3
Teams rely on coarse categories/tags that don’t capture nuanced tastes
4
Personalization experiments are slow, hard to measure, and prone to feedback loops