What are vector embedding models, and how are they used in machine learning?

Vector embeddings are dense, low‑dimensional numeric vectors that represent discrete items (words, sentences, images, users, etc.) in a continuous space. They capture semantic or relational similarity: items that are similar in meaning or context end up close together in the vector space. In machine learning, embeddings are used for tasks such as word similarity, document clustering, recommendation systems, and as input features for downstream models. Classic examples include word2vec, GloVe, FastText, and contextual embeddings from BERT or Sentence‑BERT. Embeddings enable efficient similarity search, dimensionality reduction, and improved performance for classification or regression tasks. In recommendation, user and item embeddings allow collaborative filtering without explicit ratings.