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Elasticsearch: The Definitive Guide [2.x] » Search in Depth » Controlling Relevance » function_score Query
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function_score Queryedit

The function_score query is the ultimate tool for taking control of the scoring process. It allows you to apply a function to each document that matches the main query in order to alter or completely replace the original query _score.

In fact, you can apply different functions to subsets of the main result set by using filters, which gives you the best of both worlds: efficient scoring with cacheable filters.

It supports several predefined functions out of the box:

weight
Apply a simple boost to each document without the boost being normalized: a weight of 2 results in 2 * _score.
field_value_factor
Use the value of a field in the document to alter the _score, such as factoring in a popularity count or number of votes.
random_score
Use consistently random scoring to sort results differently for every user, while maintaining the same sort order for a single user.
Decay functions—linear, exp, gauss
Incorporate sliding-scale values like publish_date, geo_location, or price into the _score to prefer recently published documents, documents near a latitude/longitude (lat/lon) point, or documents near a specified price point.
script_score
Use a custom script to take complete control of the scoring logic. If your needs extend beyond those of the functions in this list, write a custom script to implement the logic that you need.

Without the function_score query, we would not be able to combine the score from a full-text query with a factor like recency. We would have to sort either by _score or by date; the effect of one would obliterate the effect of the other. This query allows you to blend the two together: to still sort by full-text relevance, but giving extra weight to recently published documents, or popular documents, or products that are near the user’s price point. As you can imagine, a query that supports all of this can look fairly complex. We’ll start with a simple use case and work our way up the complexity ladder.

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