Yes, text-embedding-3-small can power recommendation systems effectively when “meaning of text” is a major signal—especially for content, catalogs, and cold-start scenarios. If your items have titles, descriptions, reviews, transcripts, or any other text fields, embeddings let you recommend similar items even when you have little user interaction data. This is particularly useful for “related items,” “because you read,” “similar issues,” and “next article” features where you want sensible results without building a full collaborative filtering stack.
A practical pattern is item-to-item similarity using embeddings. You generate an embedding per item (or per item chunk if items are long), store them, and query for nearest neighbors at request time or in a batch job to precompute “top N similar” lists. For example, in a developer docs portal, you can recommend related pages by embedding each page section and then aggregating section-level results into page-level recommendations. In an e-commerce-like catalog, you can embed product descriptions and retrieve similar products, then apply business rules (in-stock only, same category, price range). If you want user-personalized recommendations, you can build a lightweight user vector by averaging (or weighted averaging) embeddings of items the user interacted with, then query nearest items to that user vector. This approach is simple to implement and often performs surprisingly well for text-heavy domains.
To run this reliably in production, you typically store embeddings in a vector database such as Milvus or Zilliz Cloud. Milvus lets you do fast nearest-neighbor queries and combine them with scalar filtering (e.g., category == "observability" AND region == "JP"). You can also maintain multiple embeddings per item—title-only, description-only, or “title + key attributes”—and choose which one to query based on the context. text-embedding-3-small’s efficiency matters because recommendation systems often require many queries (per page view, per widget, per user). The main thing to watch is evaluation: track click-through rate, diversity, and “same-item duplicates,” and use A/B tests or offline similarity judgments to tune K, filtering, and any post-processing (dedupe by brand/doc_id, diversify by category, etc.).
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