Case-based reasoning (CBR) attempts to reuse past experiences to solve new problems. CBR ideas are commonplace in recommendation systems, which rely on the similarity between product queries and a case base of product cases. But, the relationship between CBR and many of these recommenders can be tenuous: the idea that product cases made up of static meta-data type features are experiential is a stretch; unless one views the type of case descriptions used by collaborative filtering (user ratings across products) as experiential. Here we explore and evaluate how to automatically generate product cases from user-generated reviews to produce cases that are based on genuine user experiences for use in a case-based product recommendation system.
