An embedding is a learned representation of data in a lower-dimensional space. It transforms high-dimensional, discrete, or symbolic data (like words, users, or items) into dense, continuous vectors that preserve semantic or structural relationships.

Why use embeddings?

• Reduce dimensionality
• Enable similarity comparison
• Improve learning by preserving structure

See: Word embedding, matrix embedding