Elasticsearch Learning to Rank Plugin
For those who don't know, Learning to Rank, is a means of using a machine learning model to optimize relevance of search results. The Elasticsearch Learning to Rank plugin creates the infrastructure for feature storage (aka templated Elastic queries), feature logging, and then uploading models trained offline for ranking with those features. This plugin is a few years old, but as one of the core maintainers, I'm eager to open the pool of contributors to this codebase used a lot by the Elastic community. I realize Elasticsearch plugins are a dark art. But if your team uses Elasticsearch for…
Elasticsearch Learning to Rank Plugin launched on December 19, 2019 with 14 votes, #737 of 2,946 launches that month and more than 75% of that year's launches.
In plain words
Elasticsearch Learning to Rank Plugin is an extension for Elasticsearch that uses machine learning models to improve search result relevance. It provides infrastructure for storing search features as templated queries, logging feature data, and deploying offline-trained ranking models. The plugin is designed for teams using Elasticsearch for search who want to implement or are already using learning-to-rank techniques to optimize result ordering.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
For those who don't know, Learning to Rank, is a means of using a machine learning model to optimize relevance of search results. The Elasticsearch Learning to Rank plugin creates the infrastructure for feature storage (aka templated Elastic queries), feature logging, and then uploading models trained offline for…
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