When case-based recommender systems use preference-based feedback, we can learn user preferences by using the trade-off relations between the preferred product and the other products in the given domain. In this work, we propose a representation for trade-offs and motivate several mechanisms by which the identified trade-offs can be used in the process of recommendation. We empirically demonstrate the effectiveness of the proposed approaches in three recommendation domains. © 2018, Springer Nature Switzerland AG.