1. A computer-based method of enabling a computer system to score recommendations for potential purchase by a customer and provide candidate recommendations, comprising: transforming a computer system programmed with code stored in a memory and executing by a processor of the computer system to configure the computer system into a machine for performing: receiving recommendation context digital data of the customer; processing the recommendation context digital data, by evaluating which association rules match the recommendation context digital data and removing association rules with a filter that do not meet a recommendation strength threshold, to identify a plurality of candidate recommendations that match the recommendation context digital data in accordance with association rules not removed, where each candidate recommendation recommends at least one recommended item; determining a computer system useable score for each candidate recommendation by applying a probabilistic purchase decision model to score an effectiveness of each candidate recommendation by comparing a recommendation outcome against a non-recommendation outcome; ranking the plurality of candidate recommendations using the score for each candidate recommendation to identify at least a highest ranking candidate recommendation; selecting the candidate recommendation having the highest score for issuance to the customer; and providing digital data representing the candidate recommendation to a computer system accessible to the customer.
1. A computer-based method of enabling a computer system to score recommendations for potential purchase by a customer and provide candidate recommendations, comprising: transforming a computer system programmed with code stored in a memory and executing by a processor of the computer system to configure the computer system into a machine for performing: receiving recommendation context digital data of the customer; processing the recommendation context digital data, by evaluating which association rules match the recommendation context digital data and removing association rules with a filter that do not meet a recommendation strength threshold, to identify a plurality of candidate recommendations that match the recommendation context digital data in accordance with association rules not removed, where each candidate recommendation recommends at least one recommended item; determining a computer system useable score for each candidate recommendation by applying a probabilistic purchase decision model to score an effectiveness of each candidate recommendation by comparing a recommendation outcome against a non-recommendation outcome; ranking the plurality of candidate recommendations using the score for each candidate recommendation to identify at least a highest ranking candidate recommendation; selecting the candidate recommendation having the highest score for issuance to the customer; and providing digital data representing the candidate recommendation to a computer system accessible to the customer. 6. The method of claim 1 wherein determining a score for each candidate recommendation by applying a probabilistic purchase decision model to score an effectiveness of each candidate recommendation by comparing a recommendation outcome against a non-recommendation outcome comprises: using a Bayesian network to calculate a first purchase probability measure for a candidate recommendation item, given a relevance measure for the item, an exposure measure for the item if the candidate recommendation is displayed and a clarity measure for the item if the candidate recommendation is displayed; using a Bayesian network to calculate a second purchase probability for the candidate recommendation item, given a relevance measure for the item, an exposure measure for the item if the candidate recommendation is not displayed and a clarity measure for the item if the candidate recommendation is not displayed; subtracting the second purchase probability measure from the first purchase probability measure to form a first difference value; and multiplying the first difference value by a margin value for the candidate recommendation item.