
Open-source personalized ranking and recommendation engine
Metarank is an open-source machine learning service for personalized ranking, search, and recommendations. It integrates with Elasticsearch and OpenSearch to add real-time personalization based on user signals like clicks and purchases, with low-latency reranking in 10-20ms.
Integrates customer signals like clicks and purchases to optimize search result ordering for maximal CTR
Uses LLMs in bi-encoder and cross-encoder modes to understand the true meaning of search queries
Generates similar-item and personalized recommendations using collaborative filtering and semantic methods
Tracks visitor profiles and adapts search results to user actions in real time
Handles large result sets within 10-20ms reranking latency, optimized for production workloads
Stateless cloud-native architecture with Redis-managed state, capable of thousands of requests per second
Simple YAML-based configuration and JSON API for quick setup without requiring ML expertise
Personalize product search results and category listings based on individual shopper behavior and purchase history
Rank articles and content feeds based on reader preferences, click patterns, and engagement signals
Add LLM-powered query understanding to existing Elasticsearch or OpenSearch deployments for better search relevance
Generate personalized friend, group, or content recommendations in social platforms
Native integration with popular search engines for Learn-to-Rank and hybrid search workflows
Optimize listing order in marketplaces to maximize user engagement and conversion rates

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