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* Term Stats documentation
* Update docs/reference/reranking/learning-to-rank-model-training.asciidoc
Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
* Fix query example.
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Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
(cherry picked from commit 0416812456)
Co-authored-by: Aurélien FOUCRET <aurelien.foucret@gmail.com>
63 lines
2.3 KiB
Text
63 lines
2.3 KiB
Text
[[learning-to-rank-search-usage]]
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=== Search using Learning To Rank
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++++
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<titleabbrev>Search using LTR</titleabbrev>
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++++
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NOTE: This feature was introduced in version 8.12.0 and is only available to certain subscription levels.
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For more information, see {subscriptions}.
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[discrete]
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[[learning-to-rank-rescorer]]
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==== Learning To Rank as a rescorer
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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>>:
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[source,console]
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----
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GET my-index/_search
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{
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"query": { <1>
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"multi_match": {
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"fields": ["title", "content"],
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"query": "the quick brown fox"
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}
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},
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"rescore": {
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"learning_to_rank": {
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"model_id": "ltr-model", <2>
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"params": { <3>
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"query_text": "the quick brown fox"
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}
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},
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"window_size": 100 <4>
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}
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}
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----
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// TEST[skip:TBD]
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<1> First pass query providing documents to be rescored.
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<2> The unique identifier of the trained model uploaded to {es}.
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<3> Named parameters to be passed to the query templates used for feature.
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<4> The number of documents that should be examined by the rescorer on each shard.
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[discrete]
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[[learning-to-rank-rescorer-limitations]]
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===== Known limitations
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[discrete]
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[[learning-to-rank-rescorer-limitations-window-size]]
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====== Rescore window size
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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`.
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[discrete]
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[[learning-to-rank-rescorer-limitations-pagination]]
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====== Pagination
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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.
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[discrete]
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[[learning-to-rank-rescorer-limitations-negative-scores]]
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====== Negative scores
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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.
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