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Explaining recommendations helps users to make better decisions. We describe a novel approach to explanation for recommender systems, one that drives the recommendation ranking process, while at the same time providing the user with useful insights into the reason why items have been recommended and the trade-offs they may need to consider when making their choice. We describe this approach in the context of a case-based recommender system that harnesses opinions mined from user-generated reviews, and evaluate it on TripAdvisor hotel data.