Product Embeddings and Item Similarity

Embeddings map items, users, or queries into a vector space that a model learns to make useful for a task. Google’s recommendation material explains that content-based and collaborative systems can represent items and queries with embeddings, then retrieve candidates using cosine, dot product, or Euclidean distance. In collaborative filtering, learned user and item vectors can approximate interaction patterns; in content-based systems, item features can contribute to representation. A product embedding is therefore not simply a hand-assigned list of product attributes. The geometric interpretation depends on the training objective and similarity measure. Cosine compares vector direction, while dot product also reflects vector magnitude; in Google’s guide, that norm sensitivity can emphasize frequent items. A nearest neighbor may be useful for candidate generation or related-item discovery, but it is not proof that products are substitutes, compatible, equally safe, or interchangeable. The system must be evaluated against the product task and user outcome. In practice, teams build embeddings from signals such as catalog content or interactions, index vectors for retrieval, and combine candidate scores with ranking features and business constraints. New or sparsely observed items present a cold-start challenge because the model may not have enough interaction evidence to learn a useful vector. Content features or exploration strategies can help, but the choice depends on the catalog and objective. Treat vector similarity as one signal, measure relevance and errors, and verify how the embedding was trained before drawing product conclusions.