Recommender Systems with Zilliz Cloud Vector Database
Explore the key role of Zilliz Cloud in seamlessly retrieving unstructured data and ensuring personalized recommendations across diverse online applications.
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.
질리즈가 추천 시스템을 지원하는 방법
Zilliz 기반 이커머스 추천 엔진은 다음과 같은 방식으로 작동합니다:
- 사용자의 구매 행동과 상품 관련 데이터는 임베딩 모델을 통해 임베딩으로 변환됩니다.
- 이러한 임베딩은 저장 및 검색을 위해 질리즈 클라우드(완전 관리형 Milvus)에 수집됩니다.
- 질리즈는 사용자와 제품 벡터 사이의 공간적 거리를 비교하여 가장 연관성이 높은 상위 K개의 결과를 검색합니다.
- 필터링, 점수화, 주문 프로세스를 거친 후 추천 시스템은 사용자에게 관심 있는 추천 상품을 제공합니다.
성공 사례
전 세계 업계 리더들은 Milvus 또는 Zilliz(완전 관리형 Milvus)를 사용하여 추천 시스템을 강화합니다.
10x faster
in vector retrieval speed
> 95%
in news classification accuracy
Less memory consumption
for faster responses and reduced cost
< 10ms
P99 latency to enable real-time data retrieval
100+ Million
Daily vector updates without performance degradation
Increase revenue
Serving more relevant ads led to improved click-through rate
10x Faster Query Speed
than the previous Elasticsearch solution
<30ms Response Time
for searching on millions of vectors
Optimized User Experience
with more accurate recommendations based on users’ purchase behaviors
10x faster
in vector retrieval speed
> 95%
in news classification accuracy
Less memory consumption
for faster responses and reduced cost
< 10ms
P99 latency to enable real-time data retrieval
100+ Million
Daily vector updates without performance degradation
Increase revenue
Serving more relevant ads led to improved click-through rate