Foundations
Embeddings and retrieval
Represent similarity with a learned vector space.
Understand the problem
An embedding maps an item into a vector. Candidate retrieval uses approximate nearest-neighbor search, then applies metadata filtering and reranking. Evaluate retrieval recall separately from final ranking.
Make it concrete
A video system retrieves candidates near a user vector, then ranks using context and freshness.
Trade-offs and pitfalls
Smaller embeddings reduce memory and latency but may lose useful signal.
Check your understanding
Explain the cold-start strategy for a new video.
Practice this topic