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.