Vipshop's Personalized Recommender System
Vipshop is a leading online discount retailer for brands in China. The company offers high quality and popular branded products to consumers throughout China at a significant discount from retail prices. To optimize the shopping experience for their customers, the company built a personalized search recommendation system based on user query keywords and user portraits. Milvus, an open-source vector database built by Zilliz, is used to power the personalized recommender system on Vipshop’s mobile apps.
Objective
Build an 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.
Challenges
- Long computational response time - the average latency to retrieve TopK results from millions of items is around 300 ms. - High maintenance cost of ElasticSearch indexes - the same set of indexes is used for both commodity feature vectors and other related data, which hardly facilitates index building, but produces a massive amount of data.
Why verctor database
- Support for various mainstream indexes makes vector search blazing fast. - Support for incremental and entire data update. - Support for distributed deployment, multi-language SDKs, and read/write separation.
Results
- Searches on million-vector datasets complete in 30 milliseconds, approximately ten times faster than the previous solution adopted by Vipshop. - Provides accurate recommendations to users to optimize their shopping experience.
"At present, Milvus-based vector search can be used steadily in recommendation system scenarios, and its high performance gives us more room to play in the dimensionality of model and algorithm selection."
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