Function Score Query

The script_score function allows you to wrap another query and customize the scoring of it optionally with a computation derived from other numeric field values in the doc using a script expression. Here is a simple sample:

"script_score" : {
    "script" : "_score * doc['my_numeric_field'].value"
}

On top of the different scripting field values and expression, the _score script parameter can be used to retrieve the score based on the wrapped query.

Scripts are cached for faster execution. If the script has parameters that it needs to take into account, it is preferable to reuse the same script, and provide parameters to it:

"script_score": {
    "lang": "lang",
    "params": {
        "param1": value1,
        "param2": value2
     },
    "script": "_score * doc['my_numeric_field'].value / pow(param1, param2)"
}

Note that unlike the custom_score query, the score of the query is multiplied with the result of the script scoring. If you wish to inhibit this, set "boost_mode": "replace"

The weight score allows you to multiply the score by the provided weight. This can sometimes be desired since boost value set on specific queries gets normalized, while for this score function it does not.

"weight" : number

The random_score generates scores using a hash of the _uid field, with a seed for variation. If seed is not specified, the current time is used.

"random_score": {
    "seed" : number
}

The field_value_factor function allows you to use a field from a document to influence the score. It’s similar to using the script_score function, however, it avoids the overhead of scripting. If used on a multi-valued field, only the first value of the field is used in calculations.

As an example, imagine you have a document indexed with a numeric popularity field and wish to influence the score of a document with this field, an example doing so would look like:

"field_value_factor": {
  "field": "popularity",
  "factor": 1.2,
  "modifier": "sqrt"
}

Which will translate into the following formula for scoring:

sqrt(1.2 * doc['popularity'].value)

There are a number of options for the field_value_factor function:

Keep in mind that taking the log() of 0, or the square root of a negative number is an illegal operation, and an exception will be thrown. Be sure to limit the values of the field with a range filter to avoid this, or use log1p and ln1p.

Decay functions score a document with a function that decays depending on the distance of a numeric field value of the document from a user given origin. This is similar to a range query, but with smooth edges instead of boxes.

To use distance scoring on a query that has numerical fields, the user has to define an origin and a scale for each field. The origin is needed to define the “central point” from which the distance is calculated, and the scale to define the rate of decay. The decay function is specified as

"DECAY_FUNCTION": {
    "FIELD_NAME": {
          "origin": "11, 12",
          "scale": "2km",
          "offset": "0km",
          "decay": 0.33
    }
}

where DECAY_FUNCTION can be "linear", "exp" and "gauss" (see below). The specified field must be a numeric field. In the above example, the field is a Geo Point Type and origin can be provided in geo format. scale and offset must be given with a unit in this case. If your field is a date field, you can set scale and offset as days, weeks, and so on. Example:

    "DECAY_FUNCTION": {
        "FIELD_NAME": {
              "origin": "2013-09-17",
              "scale": "10d",
              "offset": "5d",
              "decay" : 0.5
        }
    }

The format of the origin depends on the Date Format defined in your mapping. If you do not define the origin, the current time is used.

The offset and decay parameters are optional.

In the first example, your documents might represents hotels and contain a geo location field. You want to compute a decay function depending on how far the hotel is from a given location. You might not immediately see what scale to choose for the gauss function, but you can say something like: "At a distance of 2km from the desired location, the score should be reduced by one third." The parameter "scale" will then be adjusted automatically to assure that the score function computes a score of 0.5 for hotels that are 2km away from the desired location.

In the second example, documents with a field value between 2013-09-12 and 2013-09-22 would get a weight of 1.0 and documents which are 15 days from that date a weight of 0.5.

The DECAY_FUNCTION determines the shape of the decay:

where is computed to assure that the score takes the value decay at distance scale from origin+-offset

where again the parameter is computed to assure that the score takes the value decay at distance scale from origin+-offset

where again the parameter s is computed to assure that the score takes the value decay at distance scale from origin+-offset

In contrast to the normal and exponential decay, this function actually sets the score to 0 if the field value exceeds twice the user given scale value.

For single functions the three decay functions together with their parameters can be visualized like this (the field in this example called "age"):

If a field used for computing the decay contains multiple values, per default the value closest to the origin is chosen for determining the distance. This can be changed by setting multi_value_mode.

Example:

    "DECAY_FUNCTION": {
        "FIELD_NAME": {
              "origin": ...,
              "scale": ...
        },
        "multi_value_mode": "avg"
    }

When choosing gauss as the decay function in the above example, the contour and surface plot of the multiplier looks like this:

Suppose your original search results matches three hotels :

"Drink n Drive" is pretty far from your defined location (nearly 2 km) and is not too cheap (about 13 Euros) so it gets a low factor a factor of 0.56. "BnB Bellevue" and "Backback Nap" are both pretty close to the defined location but "BnB Bellevue" is cheaper, so it gets a multiplier of 0.86 whereas "Backpack Nap" gets a value of 0.66.

When choosing exp as the decay function in the above example, the contour and surface plot of the multiplier looks like this:

When choosing linear as the decay function in the above example, the contour and surface plot of the multiplier looks like this: