Macro and micro applications of case-based reasoning to feature-based product selection

This paper examines alternative applications of case-based reasoning to product selection. It is motivated by shortfalls in e-commerce solutions but addresses wider issues in the selection of case representations and reasoning strategies where solution requirements are negotiable and the number of potential features are large. Two approaches are examined: the micro approach uses the traditional CBR nearest-neighbour algorithm to augment product searches, while the macro approach is based on the traditional CBR-cycle but substitutes crisp constraint relaxation for similarity based retrieval. While the discussion is set against the background of a specific case study, the results have a wider applicability across a number of problem domains.