How do I build a recommendation system with OpenAI embeddings?

To build a recommendation system using OpenAI embeddings, start by converting your items (products, articles, etc.) into numerical vectors using OpenAI’s embedding models like text-embedding-3-small or text-embedding-3-large. These embeddings capture semantic meaning, allowing you to compare items based on similarity. For example, if you’re recommending movies, you could generate embeddings for movie descriptions, titles, or genres. Once items are embedded, store them in a vector database (like Pinecone, Milvus, or FAISS) optimized for fast similarity searches. When a user interacts with an item (e.g., clicks a product), convert that item into an embedding and query the database for the nearest neighbors to find similar items.

Next, implement a retrieval mechanism. For instance, if a user views a sci-fi movie, your system could take the movie’s embedding, search the database for embeddings with the smallest cosine distance (a measure of similarity), and return the top matches. You can also personalize recommendations by combining user behavior data with embeddings. For example, average the embeddings of movies a user has watched to create a “user profile” vector, then find items similar to this profile. To improve accuracy, use techniques like filtering results by metadata (e.g., excluding R-rated movies for a child profile) or re-ranking candidates with a secondary model.

Finally, test and iterate. Measure performance using metrics like click-through rate or precision@k (how often recommended items are clicked or relevant). For example, if users frequently ignore recommendations for “action movies” when they’ve watched a comedy, adjust the embedding inputs (e.g., include genre tags more prominently) or tweak the similarity threshold. Optimize the vector database for speed and scalability—if your system slows down with 1 million items, consider approximate nearest neighbor (ANN) algorithms. Tools like OpenAI’s API simplify embedding generation, but ensure you handle rate limits and batch requests efficiently. With careful tuning, embeddings can power recommendations that feel intuitive and context-aware.