Accelerating Candidate Generation in Recommender Systems Using Milvus paired with PaddlePaddle

What this post added

This post details the integration of Milvus with PaddlePaddle for building a product recommender system. It explains the candidate generation and ranking stages of recommender systems, the architecture involving MIND, PaddleRec, and Milvus, and the implementation steps including data processing, model training, and generating product item candidates. Specifically, it describes how item vectors are stored in Milvus and how user vectors are used for similarity search and ranking.