Yes, text-embedding-3-large can effectively support recommendation systems, especially when recommendations are driven by textual meaning rather than user interaction history alone. It is particularly useful for content-based recommendations, cold-start scenarios, and systems where items have rich text descriptions.
A common pattern is item-to-item recommendation. Each item—such as an article, document, product, or ticket—is embedded using text-embedding-3-large. When a user views one item, the system retrieves the nearest embeddings to recommend similar items. This works well for “related articles,” “similar issues,” or “you may also be interested in” features. Because the embeddings capture nuanced meaning, recommendations tend to feel more relevant than simple keyword overlap. User-level recommendations can also be built by averaging embeddings of items a user interacted with, creating a lightweight user profile vector.
At scale, these embeddings are stored and queried using a vector database such as Milvus or Zilliz Cloud. Milvus enables fast nearest-neighbor search even across millions of items and supports metadata filtering, which is important for enforcing business rules like category, region, or access level. text-embedding-3-large is a good fit when recommendation quality matters more than minimal compute cost, and when text content is a primary signal.
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