Accelerating Candidate Generation in Recommender Systems Using Milvus paired with PaddlePaddle

This article introduces an open-source vector database, Milvus, paired with PaddlePaddle, a deep learning platform, to address the issues faced in developing recommender systems. The basic workflow of a recommender system involves candidate generation and ranking stages. The product recommender system project uses three components: MIND (Multi-Interest Network with Dynamic Routing for Recommendation at Tmall), PaddleRec, and Milvus. MIND is an algorithm developed by Alibaba Group that processes multiple interests of one user during the candidate generation stage. PaddleRec is a large-scale search model library for recommendation, while Milvus is a vector database featuring a cloud-native architecture used for vector similarity search and vector management in this project. The system implementation involves data processing, model training, model testing, generating product item candidates, and data storage and search.

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