Getting Started with Elasticsearch Learning to Rank

The Elasticsearch Learning to Rank plugin is a powerful tool that leverages machine learning to enhance the relevance ranking of search results. By using this plugin, various organizations like the Wikimedia Foundation and Snagajob have significantly improved their search functionalities. In this article, we will explore how to set up and use this plugin to improve your search results.

What This Plugin Does

The Elasticsearch Learning to Rank plugin offers several features to improve search rankings:

• Stores features (Elasticsearch query templates) in Elasticsearch.
• Logs feature scores (relevance scores) to create a training set for offline model development.
• Stores various ranking models (Linear, XGBoost, or Ranklib) within Elasticsearch that utilize the stored features.
• Ranks search results using a stored model.

Where’s the Docs?

For detailed information about the basics of learning to rank and the benefits of this plugin, we recommend taking some time to read the docs. Regular trainings on Elasticsearch Learning to Rank are also available, supporting the free work behind this plugin.

Jumping Into the Action

If you’re eager to get started, you can find the demo in the Hello LTR repository, which provides examples for both Elasticsearch and Solr. Just follow the steps in the README to set up your environment and kick off your journey using the notebook. Have fun exploring!

Installing the Plugin

To install the plugin, you’ll need to check out the full list of prebuilt versions. Select the version that matches your Elasticsearch setup. If a suitable version isn’t available, you can follow the steps for building it yourself or file a request through the issues page.

To install the plugin, run a command similar to the following, taking care to replace the URL with the relevant 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

Keep in mind, you may need to confirm some security exceptions during the installation. You can save time by passing the -b flag to the elasticsearch-plugin command for automatic installation. And, if you already have Elasticsearch running, don’t forget to restart it!

Troubleshooting

As with any software, you might run into some issues during your setup or use of the plugin. We recommend checking the known issues document to see if there are any documented problems or workarounds for common issues.

For further assistance, consider connecting with the community or exploring resources at **[fxis.ai](https://fxis.ai)** for insights and collaboration opportunities.

Build and Deploy Locally

If you’re interested in digging deeper into the code or building a version that isn’t available, you can run a build and installation process yourself with the following command:

.gradlew clean check && .bin/elasticsearch-plugin install file:pathto/elasticsearch-learning-to-rank/build/distributions/ltr-LTR-VER-esES-VER.zip

Contributions

The development community behind this plugin thrives on contributions. If you’re interested in helping out, whether it’s coding, documentation, or other areas, be sure to read the contributing guidelines.

Elastic Release Support

This plugin aims to support all *.*.1 releases of Elasticsearch officially. If you require compatibility for early releases or support for a version not currently covered, please consider submitting a pull request.

Thanks to Our Contributors

The Elasticsearch Learning to Rank plugin was initially developed at OpenSource Connections and has seen significant contributions from organizations such as Wikimedia Foundation, Snagajob Engineering, Bonsai, and Yelp Engineering.

Special thanks to Jettro Coenradie for porting the project to ES 6.1.

At **[fxis.ai](https://fxis.ai)**, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.