Rank Feature Configuration

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For some rank features, it is possible to set configuration variables for how the features are calculated. For features which are per field or attribute, the variables are set separately per field/attribute.

Variables

Rank Features configuration variables are set by adding the following to the rank profile:

rank-properties {
    <featurename>.<configuration-property>: <value>
}

Where <featurename> is the name of a feature class (feature name up to the first dot), <configuration-property> is a property from the list below, appropriate for the feature, and <value> is either a number of a quoted string. Example: set some properties on the fieldMatch and bm25 feature class of two different fields:

rank-properties {
    fieldMatch(title).maxAlternativeSegmentations: 10
    fieldmatch(title).maxOccurrences: 5
    fieldMatch(description).maxOccurrences: 20
    bm25(title).k1: 1.5
    bm25(title).b: 0.85
}

Note that rank profiles can be inherited to use the same variables in multiple profiles.

Configuration Properties

Feature	Parameter	Default	Description
term	numTerms	5	The number of terms for which this is included in the rank features dump in the summary
bm25	k1	1.2	Used to limit how much a single query term can affect the score for a document.
	b	0.75	Used to control the effect of the field length compared to the average field length.
fieldMatch	proximityLimit	10	The maximum allowed gap within a segment.
	proximityTable	1/(2^(i/2)/3) for i in 9..0 followed by 1/2^(i/2) for i in 0..10	The proximity table deciding the importance of separations of various distances, The table must have size proximityLimit*2+1, where the first half is for reverse direction distances. The table must only contain values between 0 and 1, where 1 is "perfect" and 0 is "worst".
	maxAlternativeSegmentations	10000	The maximum number of alternative segmentations allowed in addition to the first one found. This will prefer to not consider iterations on segments that are far out in the field, and which starts late in the query.
	maxOccurrences	100	The number of occurrences the number of occurrences of each word is normalized against. This should be set as the number above which additional occurrences of the term has no real significance.
	proximityCompletenessImportance	0.9	A number between 0 and 1 which determines the importancy of field completeness in relation to query completeness in the match and completeness metrics.
	relatednessImportance	0.9	The normalized importance of relatedness used in the match metric.
	earlinessImportance	0.05	The importance of the match occuring early in the query, relative to segmentProximityImportance, occurrenceImportance and proximityCompletenessImportance in the match metric.
	segmentProximityImportance	0.05	The importance of multiple segments being close to each other, relative to earlinessImportance, occurrenceImportance and proximityCompletenessImportance in the match metric.
	occurrenceImportance	0.05	The importance of having many occurrences of the query terms, relative to earlinessImportance, segmentProximityImportance and proximityCompletenessImportance in the match metric.
	fieldCompletenessImportance	0.05	A number between 0 and 1 which determines the importancy of field completeness in relation to query completeness in the match and completeness metrics.
fieldTermMatch	numTerms	5	The number of terms for which this is included in the rank features dump in the summary
	numTerms.<fieldName>	5	The number of terms for which this is included in the rank features dump in the summary for the specified field
elementCompleteness	fieldCompletenessImportance	0.5	Higher values favor field completeness, lower values favor query completeness. Adjusting this parameter will also affect which element is selected as the best.
elementSimilarity	output.default	max((0.35*p+0.15*o+0.30*q+0.20*f)*w)	Describes how the default output should be calculated. The value must be on the form aggregator(expression). The expression is used to combine the low-level similarity measures between the query and individual elements in the field. The aggregator will be used to aggregate the output of the expression across elements. The available aggregators are max, avg and sum. The available expression operators are +, -, *, / and ^. Parenthesis may be used to override default operator precedence. Note that you must quote the expression using "expression". Terminals can be numbers or any of the following symbols: p normalized proximity measure o normalized term ordering measure q normalized query coverage f normalized field coverage w element weight
	output.name	N/A	Define an additional feature output called name. The value describes how the output should be calculated and has the same syntax as the default output described above.
attributeMatch	fieldCompletenessImportance	0.05	A number between 0 and 1 which determines the importance of field completeness in relation to query completeness in the match and completeness metrics.
	maxWeight	256	The maximal weight when calculating attributeMatch(<name>).normalizedWeight. Weights higher than this will not have any effect on this feature.
closeness	maxDistance	9013305.0	The maximal distance when calculating closeness(<name>). Distances higher than this will not have any effect on this feature. The default is about 1000 km (1 km is about 9013.305 microdegrees).
	scaleDistance	45066.525	Deprecated; use halfResponse instead. Basic scale for distances when calculating closeness(<name>).logscale. The default is about 5 km.
	halfResponse	593861.739	The distance that should give an output of 0.5 when calculating closeness(<name>).logscale. The default is about 65.89 km (must be in the range [1, maxDistance/2>). Use this parameter to fine tune the distance range where half of the dynamics of the logscale function will be used.
freshness	maxAge	3*30*24*60*60	The maximal age in seconds when calculating freshness(<name>). Ages older than this will not have any effect on this feature. The default is about 3 months.
	halfResponse	7*24*60*60	The age in seconds that should give an output of 0.5 when calculating freshness(<name>).logscale. The default is 7 days (must be in the range [1, maxAge/2>). Use this parameter to fine tune the age range where half of the dynamics of the logscale function will be used.
random	seed	Current time in microseconds	The random seed.
randomNormal	seed	Current time in microseconds	The random seed for randomNormal.
foreach	maxTerms	16	Specifies how many query term indices to iterate over ([0, maxTerms>) when using dimension terms.