Two-Tower Retrieval and Deep Ranking
~50 min read
Designing the candidate generation and ranking stages: two-tower architecture, multi-source retrieval, multi-task ranking model, and feature engineering.
Two-Tower Retrieval and Deep Ranking is a Pro topic
Sign in, then upgrade to Pro or Power to unlock this topic and the full AI Engineering curriculum.
Key points
- •Two-tower models pre-compute video embeddings offline so only the user embedding is computed at query time — this is what enables < 10ms retrieval from 10M videos
- •Multi-source candidate generation (2-tower + co-watch + content-based + trending) is essential — no single source has both personalized and fresh coverage
- •Multi-task ranking (predict watch time + like + share - abandon as separate heads) allows business-goal tuning via weight adjustment without retraining the model