Turbocharge Recommendations with Unmatched Speed
Achieve a 10x performance boost on Zilliz Cloud with advanced indexing algorithms, ensuring millisecond-level latency for prompt user interactions. Elevate your experience now!
A Real-Time Personalized Recommender System
Ensure consistently up-to-date recommendation results with unparalleled precision using range search capability, seamless data flows, and streamlined data management within Zilliz Cloud.
Effortless Scaling for Limitless Business Expansion
Say goodbye to data size concerns with Zilliz Cloud. Our clusters seamlessly scale with your budget and evolving needs. Independently scale computing and storage for effortlessly handling tens of billions of vectors.
Start with Simplicity and Amplified Productivity
Launch a large-scale similarity search service in minutes with user-friendly SDKs in multiple languages and streamlined data operations, ensuring an intuitive experience for developers.
Ensure Unwavering System Resilience
Building recommender systems with Zilliz Cloud ensures uninterrupted services even in unexpected events through multiple replicas, component isolation, and robust backup and sync capabilities.
How Zilliz Powers Recommender Systems
A Zilliz-powered e-commerce recommendation engine works in the following way:
- Users’ purchase behaviors and product-related data are transformed into embeddings through an embedding model.
- These embeddings are ingested into Zilliz Cloud (the fully managed Milvus) for storage and retrieval.
- Zilliz retrieves the Top-K most relevant results by comparing the spatial distance between the user and product vectors.
- After filtering, scoring, and ordering processes, the recommender system provides users with recommended products they are interested in.
Industry leaders worldwide use Milvus or Zilliz (fully managed Milvus) to empower their recommender systems.
VIPSHOP Builds a 10x Faster Personalized Recommender System Using MilvusRead Now
10x Faster Query Speed
<30ms Response Time
Optimized User Experience
with more accurate recommendations based on users’ purchase behaviors