1. A computer-based method of scoring recommendations for potential purchase by a customer, comprising: receiving a recommendation context from a customer; using the recommendation context to identify a plurality of candidate recommendations that match the recommendation context, where each candidate recommendation recommends at least one recommended item; with a computer system, determining a score for each candidate recommendation by subtracting a first expected margin value factor for the recommended item that is based on the candidate recommendation not being displayed from a second expected margin value factor for the recommended item that is based on the candidate recommendation being displayed; and ranking the plurality of candidate recommendations using the score for each candidate recommendation to identify at least a highest ranking candidate recommendation.
1. A computer-based method of scoring recommendations for potential purchase by a customer, comprising: receiving a recommendation context from a customer; using the recommendation context to identify a plurality of candidate recommendations that match the recommendation context, where each candidate recommendation recommends at least one recommended item; with a computer system, determining a score for each candidate recommendation by subtracting a first expected margin value factor for the recommended item that is based on the candidate recommendation not being displayed from a second expected margin value factor for the recommended item that is based on the candidate recommendation being displayed; and ranking the plurality of candidate recommendations using the score for each candidate recommendation to identify at least a highest ranking candidate recommendation. 8. The method of claim 1 , where the step of determining a score for each candidate recommendation 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.