What is the role of embeddings in modern data mining systems?

Embeddings transform categorical or unstructured data into dense vector representations.

Embeddings map high-dimensional sparse inputs (like text, users, or items) into dense continuous vector spaces where semantic similarity is preserved. This enables efficient similarity search, clustering, and predictive modeling. Techniques include Word2Vec, item embeddings, and deep learning-based representation learning.