In this paper we build on recent work on case-based product recommendation focused on generating rich product descriptions for use in a recommendation context by mining user-generated reviews. This is in contrast to conventional case-based approaches which tend to rely on case descriptions that are based on available meta-data or catalog descriptions. By mining user-generated reviews we can produce product descriptions that reflect the opinions of real users and combine notions of similarity and opinion polarity (sentiment) during the recommendation process. In this paper we compare different variations on our review-mining approach, one based purely on features found in reviews, one seeded by features that are available from meta-data, and one hybrid approach that combines both approaches. We evaluate these approaches across a variety of datasets form the travel domain.