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 Scope of the plugin documentation.

Core concepts and setup

Before implementing LTR, familiarize yourself with the foundational concepts and architecture:

• ML ranking core concepts: Understand the fundamental concepts behind Learning to Rank.
• Scope of the plugin: Learn how LTR integrates with your OpenSearch infrastructure.

Feature engineering and model development

Create and train your ranking models using the following workflow:

• Feature engineering: Design effective features for your ranking models.
• Working with features: Create and manage feature sets.
• Logging feature scores: Collect feature data for model training.
• Uploading trained models: Build and train your ranking models.

Deployment and advanced topics

Once your models are trained, deploy them in production and explore advanced features:

• Optimizing search with LTR: Deploy models in production search.
• Advanced functionality: Explore advanced LTR features and techniques.
• Common issues: Common questions and troubleshooting.