Building a personalised recommendation engine with embeddings
Embedding-based recommendation systems outperform collaborative filtering at cold-start and can be built entirely with open-source tools. This guide walks through the full pipeline: generating item and user embeddings, storing them in pgvector, and serving low-latency personalised recommendations in production.
embeddingsrecommendationsvector-searchpgvectorpersonalization
16 July 2026