Learning to Rank

The Learning to Rank plugin for OpenSearch enables you to use machine learning (ML) and behavioral data to fine-tune the relevance of documents. It uses models from the XGBoost and RankLib libraries. These models rescore the search results, considering query-dependent features such as click-through data or field matches, which can further improve relevance.

The term learning to rank is abbreviated as LTR throughout the OpenSearch documentation when the term is used in a general sense. For the plugin developer documentation, see opensearch-learning-to-rank-base.

Getting started

The following resources can help you get started:

• If you are new to LTR, start with the ML ranking core concepts documentation.
• For a quick introduction, see the demo in hello-ltr.
• If you are familiar with LTR, start with the Integrating the plugin documentation.

Installing the plugin

Prebuilt versions of the plugin are available at https://github.com/opensearch-project/opensearch-learning-to-rank-base/releases.

If you need a version that is compatible with your OpenSearch installation, follow the instructions in the README file or create an issue.

Once you have an appropriate version, you can install the plugin using the command line shown in the following example:

./bin/opensearch-plugin install https://github.com/opensearch-project/opensearch-learning-to-rank-base/releases/download/ltr-plugin-v2.11.1-RC1/ltr-plugin-v2.11.1-RC1.zip