Embeddings are used in recommender systems to represent users, items, or features as dense vectors in a lower-dimensional space. These vectors capture relationships and patterns in the data, enabling the system to measure similarity between users and items. For example, in a movie recommendation system, embeddings might encode a user’s preferences and a movie’s genre, director, or viewer ratings into numerical vectors. By calculating the distance between these vectors (e.g., using cosine similarity), the system can identify movies that align with a user’s interests. This approach reduces the complexity of high-dimensional raw data (like user IDs or item descriptions) while preserving meaningful information for accurate recommendations.
A common application is collaborative filtering, where embeddings are learned from user-item interaction data (e.g., clicks, ratings). For instance, in matrix factorization, user and item embeddings are derived by decomposing a user-item interaction matrix into two lower-dimensional matrices. Each user embedding represents their preferences, and each item embedding represents its characteristics. When a user interacts with an item, their embeddings are updated to reflect this relationship. Similarly, in content-based systems, embeddings can represent item features like text (using techniques like Word2Vec) or images (using CNNs). Hybrid systems combine both approaches, such as using embeddings from user behavior alongside embeddings of product descriptions to improve recommendation diversity.
Implementing embeddings typically involves training models like neural networks or matrix factorization algorithms. For example, a two-tower model might use separate neural networks to generate user and item embeddings, which are then compared for similarity. Tools like TensorFlow Recommenders or PyTorch simplify this process by providing built-in layers for embedding management. Challenges include handling sparse data (e.g., new users with no interaction history) and updating embeddings in real-time to reflect changing preferences. Solutions like approximate nearest neighbor libraries (FAISS) help scale similarity searches efficiently. Embeddings also enable cross-domain recommendations—for example, using embeddings trained on music listening data to recommend podcasts. By balancing computational efficiency and accuracy, embeddings remain a foundational tool for modern recommender systems.