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Elastic Docs ›Elasticsearch Guide [7.16] ›Query DSL ›Specialized queries

Rank feature query

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Rank feature query

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Boosts the relevance score of documents based on the numeric value of a rank_feature or rank_features field.

The rank_feature query is typically used in the should clause of a bool query so its relevance scores are added to other scores from the bool query.

With positive_score_impact set to false for a rank_feature or rank_features field, we recommend that every document that participates in a query has a value for this field. Otherwise, if a rank_feature query is used in the should clause, it doesn’t add anything to a score of a document with a missing value, but adds some boost for a document containing a feature. This is contrary to what we want – as we consider these features negative, we want to rank documents containing them lower than documents missing them.

Unlike the function_score query or other ways to change relevance scores, the rank_feature query efficiently skips non-competitive hits when the track_total_hits parameter is not true. This can dramatically improve query speed.

Rank feature functions

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To calculate relevance scores based on rank feature fields, the rank_feature query supports the following mathematical functions:

If you don’t know where to start, we recommend using the saturation function. If no function is provided, the rank_feature query uses the saturation function by default.

Example request

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Index setup

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To use the rank_feature query, your index must include a rank_feature or rank_features field mapping. To see how you can set up an index for the rank_feature query, try the following example.

Create a test index with the following field mappings:

  • pagerank, a rank_feature field which measures the importance of a website
  • url_length, a rank_feature field which contains the length of the website’s URL. For this example, a long URL correlates negatively to relevance, indicated by a positive_score_impact value of false.
  • topics, a rank_features field which contains a list of topics and a measure of how well each document is connected to this topic 
PUT /test
{
  "mappings": {
    "properties": {
      "pagerank": {
        "type": "rank_feature"
      },
      "url_length": {
        "type": "rank_feature",
        "positive_score_impact": false
      },
      "topics": {
        "type": "rank_features"
      }
    }
  }
}

Index several documents to the test index.

PUT /test/_doc/1?refresh
{
  "url": "https://en.wikipedia.org/wiki/2016_Summer_Olympics",
  "content": "Rio 2016",
  "pagerank": 50.3,
  "url_length": 42,
  "topics": {
    "sports": 50,
    "brazil": 30
  }
}

PUT /test/_doc/2?refresh
{
  "url": "https://en.wikipedia.org/wiki/2016_Brazilian_Grand_Prix",
  "content": "Formula One motor race held on 13 November 2016",
  "pagerank": 50.3,
  "url_length": 47,
  "topics": {
    "sports": 35,
    "formula one": 65,
    "brazil": 20
  }
}

PUT /test/_doc/3?refresh
{
  "url": "https://en.wikipedia.org/wiki/Deadpool_(film)",
  "content": "Deadpool is a 2016 American superhero film",
  "pagerank": 50.3,
  "url_length": 37,
  "topics": {
    "movies": 60,
    "super hero": 65
  }
}

Example query

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The following query searches for 2016 and boosts relevance scores based on pagerank, url_length, and the sports topic.

GET /test/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "content": "2016"
          }
        }
      ],
      "should": [
        {
          "rank_feature": {
            "field": "pagerank"
          }
        },
        {
          "rank_feature": {
            "field": "url_length",
            "boost": 0.1
          }
        },
        {
          "rank_feature": {
            "field": "topics.sports",
            "boost": 0.4
          }
        }
      ]
    }
  }
}

Top-level parameters for rank_feature

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field
(Required, string) rank_feature or rank_features field used to boost relevance scores.
boost

(Optional, float) Floating point number used to decrease or increase relevance scores. Defaults to 1.0.

Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.

saturation

(Optional, function object) Saturation function used to boost relevance scores based on the value of the rank feature field. If no function is provided, the rank_feature query defaults to the saturation function. See Saturation for more information.

Only one function saturation, log, sigmoid or linear can be provided.

log

(Optional, function object) Logarithmic function used to boost relevance scores based on the value of the rank feature field. See Logarithm for more information.

Only one function saturation, log, sigmoid or linear can be provided.

sigmoid

(Optional, function object) Sigmoid function used to boost relevance scores based on the value of the rank feature field. See Sigmoid for more information.

Only one function saturation, log, sigmoid or linear can be provided.

linear

(Optional, function object) Linear function used to boost relevance scores based on the value of the rank feature field. See Linear for more information.

Only one function saturation, log, sigmoid or linear can be provided.

Notes

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Saturation

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The saturation function gives a score equal to S / (S + pivot), where S is the value of the rank feature field and pivot is a configurable pivot value so that the result will be less than 0.5 if S is less than pivot and greater than 0.5 otherwise. Scores are always (0,1).

If the rank feature has a negative score impact then the function will be computed as pivot / (S + pivot), which decreases when S increases.

GET /test/_search
{
  "query": {
    "rank_feature": {
      "field": "pagerank",
      "saturation": {
        "pivot": 8
      }
    }
  }
}

If a pivot value is not provided, Elasticsearch computes a default value equal to the approximate geometric mean of all rank feature values in the index. We recommend using this default value if you haven’t had the opportunity to train a good pivot value.

GET /test/_search
{
  "query": {
    "rank_feature": {
      "field": "pagerank",
      "saturation": {}
    }
  }
}

Logarithm

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The log function gives a score equal to log(scaling_factor + S), where S is the value of the rank feature field and scaling_factor is a configurable scaling factor. Scores are unbounded.

This function only supports rank features that have a positive score impact.

GET /test/_search
{
  "query": {
    "rank_feature": {
      "field": "pagerank",
      "log": {
        "scaling_factor": 4
      }
    }
  }
}

Sigmoid

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The sigmoid function is an extension of saturation which adds a configurable exponent. Scores are computed as S^exp^ / (S^exp^ + pivot^exp^). Like for the saturation function, pivot is the value of S that gives a score of 0.5 and scores are (0,1).

The exponent must be positive and is typically in [0.5, 1]. A good value should be computed via training. If you don’t have the opportunity to do so, we recommend you use the saturation function instead.

GET /test/_search
{
  "query": {
    "rank_feature": {
      "field": "pagerank",
      "sigmoid": {
        "pivot": 7,
        "exponent": 0.6
      }
    }
  }
}

Linear

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The linear function is the simplest function, and gives a score equal to the indexed value of S, where S is the value of the rank feature field. If a rank feature field is indexed with "positive_score_impact": true, its indexed value is equal to S and rounded to preserve only 9 significant bits for the precision. If a rank feature field is indexed with "positive_score_impact": false, its indexed value is equal to 1/S and rounded to preserve only 9 significant bits for the precision.

GET /test/_search
{
  "query": {
    "rank_feature": {
      "field": "pagerank",
      "linear": {}
    }
  }
}
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