Accelerating Candidate Generation in Recommender Systems Using Milvus paired with PaddlePaddle

This post details a product recommender system project that uses Milvus for candidate generation. It explains the basic workflow of a recommender system (candidate generation and ranking), introduces the MIND algorithm for multi-interest user representation, PaddleRec for model training and deployment, and Milvus for storing and searching product item vectors. The implementation details cover data processing, model training using MIND on PaddleRec, and generating product item candidates by storing item vectors in Milvus and performing vector similarity search with user vectors.