Abstract: Machine learning is used to determine different relative component score weighting options for different service categories. A specification of a desired service in a specific service category is received. A relative component score weighting among the different relative component score weighting options is identified for the specific service category. A profile of a service provider candidate is analyzed using machine learning to determine at least one component score among a plurality of different component scores for the service provider candidate. A total score is calculated for the plurality of different component scores based on the identified relative component score weighting for the specific service category.

Abstract: Examples relate to detecting potential grey market activities. The examples disclosed herein enable storing, in a product database, a plurality of product identifiers. At least one of the plurality of product identifiers may be associated with a promotion that is intended for at least one end customer. The examples disclosed herein enable triggering a detection of a potential grey market activity in response to receiving customer registration information from a first end customer. The customer registration information may comprise a first product identifier. The detection of the potential grey market activity comprises identifying the first product identifier in the product database and the promotion associated with the first product identifier, and determining whether the first end customer is the end customer for which the promotion associated with the first product identifier is intended.