Personalized content feeds that balance relevance with discovery. Increase engagement, session length, and subscriber retention through AI that reads behavior, not just clicks.
All readers see the same editorial feed regardless of interests, reading history, or engagement patterns.
Pure relevance optimization traps readers in narrow interest loops. Content variety suffers, and discovery of new topics declines.
Click-through rates don't predict whether a reader will renew a subscription. You need models that optimize for lifetime value.
Editors want control over featured stories and sponsored content. Algorithms need to respect editorial judgment while still personalizing.
Combines collaborative filtering, content-based embeddings, and matrix factorization with a ranking layer balancing relevance and diversity
User preference vectors, content embeddings, popularity trends, and diversity metrics built from multiple engagement signals
Batch model training with near real-time serving, plus editorial controls to pin, boost, or suppress content
Per-reader content optimization based on behavior
Readers encounter familiar and novel content
Models trained on subscriber retention rather than raw clickthrough
From data analysis to production recommendation engine