metarank/metarank
A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine observed · 2026-08-28
Health v2 · maintenance only
88/100
- Activity 99
- Release rhythm 67
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 427
- age_days: 2205
- days_rel: 8
- days_push: 7
- n_releases_24m: 2
Adoption not part of the score
2434 stars · 108 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Metarank is an open-source, low-code Learning-to-Rank service that personalizes search results, listings, and recommendations using real-time user signals like clicks and purchases. It is a stateless, cloud-native service (with state in Redis) that reranks results from existing search or recommendation systems with low latency.
Use cases
- add learning-to-rank reranking to my existing search engine
- personalize search results based on user clicks and purchases
- build a 'you may also like' recommendation widget for my store
- optimize ranking for maximum CTR
- add real-time session-based personalization to listings
- use LLM bi-encoders and cross-encoders for semantic search reranking
- deploy a scalable low-latency reranking service on Kubernetes
When to choose
- you already have a search or recommendation engine and want to boost engagement with ML-based reranking
- you need real-time personalization with 10-20ms reranking latency
- you want prebuilt ranking signals (CTR, referer, user-agent, time) without writing custom feature code
- you need a horizontally scalable, cloud-native ranking service
When to avoid
- you need a full search engine rather than a reranking layer on top of one
- your stack cannot run JVM services or manage a Redis dependency
- you have no user interaction data to train or feed the ranker
- you need a fully managed SaaS rather than self-hosted infrastructure
Facets
service · maturity active
machine-learning search-engine rag llm-inference api-framework monitoring machine-learning data-science e-commerce web-development self-hosted cloud jvm learning-to-rank personalization reranking scala feature-engineering collaborative-filtering semantic-search ctr-optimization search recommendation docker kubernetes
1 source
- readme: https://github.com/metarank/metarank · fetched 2026-08-28 · e1872be829f7
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| metarank/metarank | main | 88 |
For agents
markdown · JSON · MCP: product_card(name="metarank/metarank")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem