This article was originally published on AI Study Room . For the full version with working code examples and related articles, visit the original post. AI Recommendation Systems: From Embeddings to Production Recommendation systems power personalization across e-commerce, media, and SaaS. Modern AI approaches combine embedding-based similarity, collaborative filtering, and LLM-driven reasoning to deliver relevant suggestions at scale. The Evolution of Recommendations Traditional recommender systems fell into two camps: collaborative filtering (user-item interactions) and content-based filtering (item attributes). Both have known limitations — cold start for new users, sparse interaction data, and inability to understand semantic meaning. Embedding-based recommendations solve these problems by representing users and items as dense vectors in a shared semantic space.…