The offerTieBreakMethod property defines the behavior of offer serving when two offers have equivalent (tied) scores. If you set this property to its default value of Random, presents a random choice from among the offers that have equivalent scores. If you set this configuration to Newer Offer, serves up the newer offer (based on having a higher offer ID) ahead of the older offer (lower offer ID) in the case where the scores among the offers are the same.
has an optional feature that allows the administrator to configure the system to return the offers in random order independent of the score, by setting the percentRandomSelection option (
Campaign | partitions | [partition_number] | Interact | learning | percentRandomSelection). The
offerTieBreakMethod property described here is used only when
percentRandomSelection is set to zero (disabled).
The optimizationType property defines whether uses a learning engine to assist with offer assignments. If set to
NoLearning, does not use learning. If set to
BuiltInLearning, uses the Bayesian learning engine built with . If set to
ExternalLearning, uses a learning engine you provide. If you select
ExternalLearning, you must define the
externalLearningClass and
externalLearningClassPath properties.
0 tells to use the effective date to filter the offer, so that if the offer effective date is earlier than or equal to the current date, the offer effective date, the offer is served to visitors.