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.
- Want a quickstart? Check out the demo in hello-ltr.
- Brand new to learning to rank? head to :doc:`core-concepts`.
- Otherwise, start with :doc:`fits-in`
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-1.1.0-es6.5.4.zip
(It's expected you'll confirm some security exceptions, you can pass -b to elasticsearch-plugin to automatically install)
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.
.. toctree:: :maxdepth: 2 core-concepts fits-in building-features feature-engineering logging-features training-models searching-with-your-model x-pack advanced-functionality faq :caption: Contents: