Embedding systems

Embedding systems map high-dimensional categorical data into dense, low-dimensional vectors to capture semantic relationships.

Modern embedding systems transform discrete inputs (like the word 'king' or a unique user ID) into continuous vector spaces. By representing data as points in a 300-dimensional or 768-dimensional manifold, these systems enable machines to calculate similarity via cosine distance. This technology powers Google's BERT for search intent, Spotify's Collaborative Filtering for song discovery, and OpenAI's Ada models for retrieval-augmented generation (RAG). It effectively replaces rigid keyword matching with fluid mathematical proximity.

https://www.tensorflow.org/text/guide/word_embeddings