<p>Conversational recommendation systems can greatly benefit from techniques that explain the reasons behind their actions. We propose techniques that generate explanations by resorting to an auxiliary knowledge graph and an associated knowledge embedding. By exploiting the embedding plausibility score while searching a knowledge graph, we present a method that effectively generates reasons for a recommendation. We then propose a host of techniques to generate balanced reasons both for and against a recommendation, so as to enhance user trust in a conversational recommendation system. To do so, we develop a concrete implementation of Snedegar’s theory of reasons for/against. Experiments at functional, human, and application levels demonstrate that our proposals do improve the interpretability of conversational recommendations systems with controlled computational cost.</p>