Building a Personalized Product Recommender System with Vipshop and Milvus

Vipshop, an online discount retailer in China, built a personalized search recommendation system to optimize their customers' shopping experience. The core function of the e-commerce search recommendation system is to retrieve suitable products from a large number of products and display them to users according to their search intent and preference. To achieve this, Vipshop used Milvus, an open source vector database, which supports distributed deployment, multi-language SDKs, read/write separation, etc., compared to the commonly used standalone Faiss. The overall architecture consists of two main parts: write process and read process. Data such as product information, user search intent, and user preferences are all unstructured data that were converted into feature vectors using various deep learning models and imported into Milvus. With the excellent performance of Milvus, Vipshop's e-commerce search recommendation system can efficiently query the TopK vectors that are similar to the target vectors. The average latency for recalling TopK vectors is about 30 ms.