Abstract.
This paper describes LCW, a procedure for learning customer preferences represented as feature weights by observing customers’ selections from return sets. An empirical evaluation on simulated customer behavior indicated that uninformed hypotheses about customer weights lead to low ranking accuracy unless customers place some importance on almost all features or the total number of features is quite small. In contrast, 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 customers’ 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.
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Karl Branting, L. Learning Feature Weights from Customer Return-Set Selections. Knowledge and Information Systems 6, 188–202 (2004). https://doi.org/10.1007/s10115-003-0110-0
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DOI: https://doi.org/10.1007/s10115-003-0110-0