elasticsearch/docs/reference/reranking/learning-to-rank-search-usage.asciidoc

[[learning-to-rank-search-usage]]
=== Search using Learning To Rank
++++
<titleabbrev>Search using LTR</titleabbrev>
++++

NOTE: This feature was introduced in version 8.12.0 and is only available to certain subscription levels.
For more information, see {subscriptions}.

[discrete]
[[learning-to-rank-rescorer]]
==== Learning To Rank as a rescorer

Once your LTR model is trained and deployed in {es}, it can be used as a <<rescore, rescorer>> in the <<search-your-data, search API>>:

[source,console]
----
GET my-index/_search
{
  "query": { <1>
    "multi_match": {
      "fields": ["title", "content"],
      "query": "the quick brown fox"
    }
  },
  "rescore": {
    "learning_to_rank": {
      "model_id": "ltr-model", <2>
      "params": { <3>
        "query_text": "the quick brown fox"
      }
    },
    "window_size": 100 <4>
  }
}
----
// TEST[skip:TBD]
<1> First pass query providing documents to be rescored.
<2> The unique identifier of the trained model uploaded to {es}.
<3> Named parameters to be passed to the query templates used for feature.
<4> The number of documents that should be examined by the rescorer on each shard.

[discrete]
[[learning-to-rank-rescorer-limitations]]
===== Known limitations

[discrete]
[[learning-to-rank-rescorer-limitations-window-size]]
====== Rescore window size

Scores returned by LTR models are usually not comparable with the scores issued by the first pass query and can be lower than the non-rescored score. This can cause the non-rescored result document to be ranked higher than the rescored document. To prevent this, the `window_size` parameter is mandatory for LTR rescorers and should be greater than or equal to `from + size`.

[discrete]
[[learning-to-rank-rescorer-limitations-pagination]]
====== Pagination

When exposing pagination to users, `window_size` should remain constant as each page is progressed by passing different `from` values. Changing the `window_size` can alter the top hits causing results to confusingly shift as the user steps through pages.

[discrete]
[[learning-to-rank-rescorer-limitations-negative-scores]]
====== Negative scores

Depending on how your model is trained, it’s possible that the model will return negative scores for documents. While negative scores are not allowed from first-stage retrieval and ranking, it is possible to use them in the LTR rescorer.