This paper proposes a framework for determining the return-set size that optimizes the tradeoff between requirements satisfaction (the degree to which the customer's requirements are satisfied by the best inventory item presented to the customer) and cognitive load (the number of actions a customer must perform and the number of choices from which these are actions are selected). This framework is based on LCW (Learning Customer Weights), a procedure for learning customer preferences

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... as feature weights by observing customers' selections from return sets. An empirical evaluation on simulated customer behavior indicated that LCW's estimate of the mean preferences of a customer population improved as the number of customers increased, even for larger numbers of features of widely differing importance. This improvement in the estimate of mean customer preferences led to improved prediction of individual customer's rankings, irrespective of the extent of variation among customers and whether a single or multiple retrievals were permitted. The experimental results suggest that the return set that optimizes benefit may be smaller for customer populations with little variation than for customer populations with wide variation.