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Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch
| Date | Stars |
|---|---|
| 2026-07-31 | 1522 |
| 2026-08-03 | 1522 |
| 2026-08-06 | 1522 |
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[](https://travis-ci.com/o19s/elasticsearch-learning-to-rank) The Elasticsearch Learning to Rank plugin uses machine learning to improve search relevance ranking. It's powering search at places like Wikimedia Foundation and Snagajob! # What this plugin does... This plugin: - Allows you to store features (Elasticsearch query templates) in Elasticsearch - Logs features scores (relevance scores) to create a training set for offline model development - Stores linear, xgboost, or ranklib ranking models in Elasticsearch that use features you've stored - Ranks search results using a stored model ## Where's the docs? We recommend taking time to [read the docs](http://elasticsearch-learning-to-rank.readthedocs.io). There's quite a bit of detailed information about learning to rank basics and how this plugin can ease learning to rank development. You can also participate in regular [trainings](http://opensourceconnections.com/events/training) on Elasticsearch Learning to Rank, which support the free work done on this plugin. ## I want to jump in! The demo lives in another repo now, [Hello LTR](https://github.com/o19s/hello-ltr) and it has both ES and Solr example. Follow the directions for Elasticsearch in the README to set up the environment and start with the [notebooks/elasticsearch/tmdb/hello-ltr.ipynb](https://github.com/o19s/hello-ltr/blob/master/notebooks/elasticsearch/tmdb/hello-ltr%20(ES).ipynb). Have fun! # Installing See the full list of [prebuilt versions](https://github.com/o19s/elasticsearch-learning-to-rank/releases) and select the version that matches your Elasticsearch version. If you don't see a version available, see the link below for building or file a request via [issues](https://github.com/o19s/elasticsearch-learning-to-rank/issues). To install, you'd run a command like this but replacing with the appropriate prebuilt version zip: `./bin/elasticsearch-plugin install https://github.com/o19s/elasticsearch-learning-to-rank/releases/download/v1.5.4-es7.11.2/ltr-plugin-v1.5.4-es7.11.2.zip` (It's expected you'll confirm some security exceptions, you can pass `-b` to `elasticsearch-plugin` to automatically install) If you already are running Elasticsearch, don't forget to restart! # Known issues As any other piece of software, this plugin is not exempt from issues. Please read the [known issues](KNOWN_ISSUES.md) to learn about the current issues that we are aware of. This file might include workarounds to mitigate them when possible. # Build and Deploy Locally Notes if you want to dig into the code or build for a version there's no build for, please feel free to run the build and installation process yourself: ``` ./gradlew clean check ./bin/elasticsearch-plugin install file:///path/to/elasticsearch-learning-to-rank/build/distributions/ltr-<LTR-VER>-es<ES-VER>.zip ``` # How to Contribute For more information on helping us out (we need your help!), developing with the plugin, creating docs, etc please read [CONTRIBUTING.md](/CONTRIBUTING.md). ## Elastic Release Support We do our best to officially support `*.*.1` releases of Elasticsearch. If you have a need for "dot-oh" compatibility or a version we don't support please consider submitting a PR. # Who built this? - [Initially developed](http://opensourceconnections.com/blog/2017/02/14/elasticsearch-learning-to-rank/) at [OpenSource Connections](http://opensourceconnections.com). - Significant contributions by [Wikimedia Foundation](https://wikimediafoundation.org/wiki/Home), [Snagajob Engineering](https://engineering.snagajob.com/), [Bonsai](https://bonsai.io/), and [Yelp Engineering](https://engineeringblog.yelp.com/) - Thanks to [Jettro Coenradie](https://amsterdam.luminis.eu/author/jettro/) for porting to ES 6.1 ## Other Acknowledgments & Stuff To Read - Bloomberg's [Learning to Rank work for Solr](https://issues.apache.org/jira/browse/SOLR-8542) - O
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ff76b6684c13b08e, llm:Repository topics and description: 'machine-learning', 'relevant-search', 'search-relevance', 'elasticsearch-plugin'. Description: 'Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch' and details about storing features, logging feature scores, storing/ranking with models (linear, xgboost, ranklib).
matched fp:ff76b6684c13b08e, llm:Repository topics and description: 'machine-learning', 'relevant-search', 'search-relevance', 'elasticsearch-plugin'. Description: 'Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch' and details about storing features, logging feature scores, storing/ranking with models (linear, xgboost, ranklib).
matched fp:ff76b6684c13b08e, llm:Repository topics and description: 'machine-learning', 'relevant-search', 'search-relevance', 'elasticsearch-plugin'. Description: 'Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch' and details about storing features, logging feature scores, storing/ranking with models (linear, xgboost, ranklib).