How do vector embeddings work in recommendation systems?

Vector embeddings in recommendation systems work by converting users, items, or interactions into numerical representations (vectors) in a high-dimensional space. These embeddings capture relationships and patterns in the data, allowing the system to measure similarity between entities. For example, in a movie recommendation system, each movie and user might be represented as a vector. Movies with similar genres, themes, or audience preferences will have vectors closer together in this space. Similarly, users with comparable viewing histories or preferences will also have vectors that are near each other. By calculating distances (e.g., cosine similarity) between these vectors, the system can recommend items that align with a user’s interests or identify users with similar tastes.

Embeddings are typically created using machine learning techniques like matrix factorization or neural networks. In matrix factorization, the user-item interaction matrix (e.g., ratings or clicks) is decomposed into two lower-dimensional matrices: one representing users and the other representing items. Each row in these matrices becomes the embedding for a user or item. Neural networks, such as autoencoders or transformer-based models, can generate embeddings by processing features like item descriptions, user behavior sequences, or contextual data. For instance, a product’s embedding might be derived from its text description using a language model. During training, the model adjusts these embeddings to minimize prediction errors—like how well a user’s embedding predicts their interaction with an item.

A practical example is YouTube’s recommendation system, which uses embeddings to represent videos and users. Videos are embedded based on features like watch time, tags, and user interactions, while user embeddings reflect their watch history and engagement. When a user watches a video, the system retrieves similar videos by comparing their embeddings. Embeddings also handle sparse data efficiently. For instance, in e-commerce, a user who has only interacted with a few items can still receive relevant recommendations because their embedding generalizes from limited data. By compressing complex relationships into vectors, embeddings enable scalable, real-time recommendations using efficient vector similarity calculations, even across millions of items.