18. An article of manufacture having at least one, non-transitory recordable medium having stored thereon executable code which, when executed by at least one processing device, cause the at least one processing device to: use a recommendation context associated with a customer to identify a set of candidate recommendations that match a recommendation context associated with a customer; calculate a score value for each candidate recommendation using an probabilistic model to determine a boost in expected margin value for each candidate recommendation using one or more predetermined scoring criteria; rank the set of candidate recommendations using the score value for each candidate recommendations; and issue at least the candidate recommendation rule having the highest score value as a purchase recommendation to the customer.
18. An article of manufacture having at least one, non-transitory recordable medium having stored thereon executable code which, when executed by at least one processing device, cause the at least one processing device to: use a recommendation context associated with a customer to identify a set of candidate recommendations that match a recommendation context associated with a customer; calculate a score value for each candidate recommendation using an probabilistic model to determine a boost in expected margin value for each candidate recommendation using one or more predetermined scoring criteria; rank the set of candidate recommendations using the score value for each candidate recommendations; and issue at least the candidate recommendation rule having the highest score value as a purchase recommendation to the customer. 19. The article of manufacture of claim 18 , wherein the executable instructions and data cause the at least one processing device to calculate the score value for each candidate recommendation by identifying a selling point message for each candidate recommendation that maximizes the purchase probability for the candidate recommendation if the candidate recommendation is issued, where the selling point message is identified using a probabilistic use case model to generate a probability distribution for possible use cases for the customer.