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Elasticsearch Learning to Rank: the documentation

Learning to Rank applies machine learning to relevance ranking. The Elasticsearch Learning to Rank plugin (Elasticsearch LTR) gives you tools to train and use ranking models in Elasticsearch. This plugin powers search at places like Wikimedia Foundation and Snagajob.

Get started

Installing

Pre-built versions can be found here. Want a build for an ES version? Follow the instructions in the README for building or create an issue. Once you've found a version compatible with your Elasticsearch, you'd run a command such as:

./bin/elasticsearch-plugin install \
http://es-learn-to-rank.labs.o19s.com/ltr-1.0.0-es6.1.2.zip

(It's expected you'll confirm some security exceptions, you can pass -b to elasticsearch-plugin to automatically install)

Are you using x-pack security in your cluster? we got you covered, check :doc:`x-pack` for specific configuration details.

HEEELP!

The plugin and guide was built by the search relevance consultants at OpenSource Connections in partnership with the Wikimedia Foundation and Snagajob Engineering. Please contact OpenSource Connections or create an issue if you have any questions or feedback.

Contents

.. toctree::
   :maxdepth: 2

   core-concepts
   fits-in
   building-features
   feature-engineering
   logging-features
   training-models
   searching-with-your-model
   x-pack
   advanced-functionality
   :caption: Contents:


Indices and tables