Machine learning integration for a dynamically scaling matching and prioritization engine
Abstract:
A system and related method are provided for dynamically modifying a rule-based matching system. A processor receives a source data entity, and then locates a matching data entity by a search based on the source data entity and a rule set. A rater assessment is provided by a rater that utilizes at least one factor that is independent of the rule set and comprises a degree of matching between the source and matching data entity. A revised rule set is dynamically created based on an output of the analyzer, which in turn is based on the source data entity, the matching data entity, the rater assessment, and the rule set. Once this is complete, a second matching data entity is located by searching for the second matching data entity based on the source data entity and the revised rule set.
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