What are embeddings and how do you use them for search and recommendations?

Embeddings are vector representations that capture semantic similarity. Use cases: - Semantic search - Clustering - Recommendations - Deduplication Key considerations: model choice, normalization, distance metric, and evaluation with real queries. Monitor drift and update embeddings when content changes.