Product recommendation systems are now a key part of many e-commerce services and have proven to be a successful way to help users navigate complex product spaces. In this paper, we focus on critiquing-based recommenders, which permit users to tweak the features of recommended products in order to refine their needs and preferences. In this paper, we describe a novel approach to reusing past critiquing histories in order to improve overall recommendation efficiency.