What are embeddings used for?

Embeddings are numerical representations of data that capture meaningful relationships and patterns, enabling machines to process complex information more effectively. In practice, embeddings convert high-dimensional data—like text, images, or user behavior—into dense, lower-dimensional vectors. These vectors place semantically similar items closer together in the vector space. For example, words with related meanings (e.g., “cat” and “dog”) or images of the same object class (e.g., cars) are assigned vectors that are geometrically near each other. This makes embeddings particularly useful for tasks where understanding similarity or context is critical.

One common application is in natural language processing (NLP). Word embeddings like Word2Vec or GloVE transform words into vectors, allowing models to interpret relationships like synonyms or analogies (e.g., “king - man + woman ≈ queen”). Similarly, sentence or document embeddings (e.g., using BERT or Universal Sentence Encoder) enable comparisons of entire text blocks for tasks like document clustering or semantic search. Beyond text, embeddings are used in recommendation systems: user and item embeddings—such as those in collaborative filtering—encode preferences and product features, helping identify items a user might like based on similar users’ behavior.

Embeddings also play a key role in search and retrieval systems. For instance, in image search, embeddings generated by convolutional neural networks (CNNs) allow finding visually similar images by comparing vector distances. In structured data, embeddings can represent categorical variables (e.g., product IDs) in a way that captures latent relationships, improving model performance in tasks like fraud detection. Developers often leverage pre-trained embeddings (e.g., from OpenAI’s API) to save computational resources, but custom embeddings can be trained for domain-specific needs, such as medical text analysis. By converting raw data into a computationally efficient format, embeddings bridge the gap between human-understandable information and machine learning models.