Campaign | partitions | partition[n] | Interact | learning
These configuration properties enable you to tune the built-in learning module.
confidenceLevel
Description
A percentage indicating how confident you want the learning utility to be before switching from exploration to exploitation. A value of 0 effectively shuts off exploration.
This property is applicable if the Interact > offerserving > optimizationType property for Interact runtime is set to BuiltInLearning only.
Default value
95
Valid Values
An integer between 0 and 95 divisible by 5 or 99.
enableLearning
Description
If set to Yes, the Interact design time expects learning to be enabled. If you set enableLearning to yes, you must configure Interact > offerserving > optimizationType to BuiltInLearning or ExternalLearning.
If set to No, the Interact design time expects learning to be disabled. If you set enableLearning to no, you must configure Interact > offerserving > optimizationType to NoLearning.
Default value
No
maxAttributeNames
Description
The maximum number of learning attributes the Interact learning utility monitors.
This property is applicable if the Interact > offerserving > optimizationType property for Interact runtime is set to BuiltInLearning only.
Default value
10
Valid Values
Any integer.
maxAttributeValues
Description
The maximum number of values the Interact learning module tracks for each learning attribute.
This property is applicable if the Interact > offerserving > optimizationType property for Interact runtime is set to BuiltInLearning only.
Default value
5
otherAttributeValue
Description
The default name for the attribute value used to represent all attribute values beyond the maxAttributeValues.
This property is applicable if the Interact > offerserving > optimizationType property for Interact runtime is set to BuiltInLearning only.
Default value
Other
Valid Values
A string or number.
Example
If maxAttributeValues is set to 3 and otherAttributeValue is set to other, the learning module tracks the first three values. All of the other values are assigned to the other category. For example, if you are tracking the visitor attribute hair color, and the first five visitors have the hair colors black, brown, blond, red, and gray, the learning utility tracks the hair colors black, brown, and blond. The colors red and gray are grouped under the otherAttributeValue, other.
percentRandomSelection
Description
The percent of the time the learning module presents a random offer. For example, setting percentRandomSelection to 5 means that 5% of the time (5 out of every 100 recommendations), the learning module presents a random offer, independent of the score. When learning is enabled, enabling percentRandomSelection overrides the offerTieBreakMethod configuration property.
Default value
5
Valid Values
Any integer from 0 (which disables the percentRandomSelection feature) up to 100.
recencyWeightingFactor
Description
The decimal representation of a percentage of the set of data defined by the recencyWeightingPeriod. For example, the default value of .15 means that 15% of the data used by the learning utility comes from the recencyWeightingPeriod.
This property is applicable if the Interact > offerserving > optimizationType property for Interact runtime is set to BuiltInLearning only.
Default value
0.15
Valid Values
A decimal value less than 1.
recencyWeightingPeriod
Description
The size in hours of data granted the recencyWeightingFactor percentage of weight by the learning module. For example, the default value of 120 means that the recencyWeightingFactor of the data used by the learning module comes from the last 120 hours.
This property is applicable only if optimizationType is set to builtInLearning.
Default value
120
minPresentCountThreshold
Description
The minimum number of times an offer must be presented before its data is used in calculations and the learning module enters the exploration mode.
Default value
0
Valid Values
An integer greater than or equal to zero.
enablePruning
Description
If set to Yes, the Interact learning module algorithmically determines when a learning attribute (standard or dynamic) is not predictive. If a learning attribute is not predictive, the learning module will not consider that attribute when determining the weight for an offer. This continues until the learning module aggregates learning data.
If set to No, the learning module always uses all learning attributes. By not pruning non-predictive attributes, the learning module may not be as accurate as it could be.
Default value
Yes
Valid Values
Yes | No
Campaign | partitions | partition[n] | Interact | learning | learningAttributes | [learningAttribute]