Metarank
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About
Metarank is an open-source ranking service designed to enhance search and recommendation systems using semantic neural search capabilities. It allows developers to create personalized search interactions and content recommendations through automated machine learning features. Metarank is optimized for latency and can efficiently manage large datasets with real-time personalization, integrating customer signals like clicks and purchases. It supports various functionalities, such as Learning-to-Rank and collaborative filtering for recommendations, alongside extensive configuration options and integrations with existing platforms. It is highly scalable, supports multiple deployment modes (including Docker and Kubernetes), and provides a demo for users to experience its capabilities and implementation guidelines for setting it up effectively.
Platform
Task
Features
• integration with various data sources
• real-time personalization
• personalized semantic search
• automatic feature generation
• learning-to-rank optimization
• recommendations via collaborative filtering
FAQs
What is Metarank used for?
Metarank is used to build personalized semantic search and recommendation systems.
How does Metarank improve search results?
It integrates customer signals and uses LLMs to enhance the understanding of search queries leading to smarter search results.
Is Metarank scalable?
Yes, it is optimized for latency and can scale horizontally to manage thousands of requests per second.
Pricing Plans
Free
Free Plan• Open-source access
• Integration with various platforms
• Real-time personalization
• Automated ML model retraining
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