function_score Query
The function_score query is the ultimate tool for taking control of the scoring process._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
weightof2results in2 * _score. field_value_factor- Use the value of a field in the document to alter the
_score, such as factoring in apopularitycount or number ofvotes. 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, orpriceinto the_scoreto 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.
转自：https://www.elastic.co/guide/en/elasticsearch/guide/current/function-score-query.html