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As recommendation is essentially a comparative (or ranking) process, a goodexplanation should illustrate to users why an item is believed to be betterthan another, i.e., comparative explanations about the recommended items.Ideally, after reading the explanations, a user should reach the same rankingof items as the system's. Unfortunately, little research attention has yet beenpaid on such comparative explanations.  In this work, we develop an extract-and-refine architecture to explain therelative comparisons among a set of ranked items from a recommender system. Foreach recommended item, we first extract one sentence from its associatedreviews that best suits the desired comparison against a set of referenceitems. Then this extracted sentence is further articulated with respect to thetarget user through a generative model to better explain why the item isrecommended. We design a new explanation quality metric based on BLEU to guidethe end-to-end training of the extraction and refinement components, whichavoids generation of generic content. Extensive offline evaluations on twolarge recommendation benchmark datasets and serious user studies against anarray of state-of-the-art explainable recommendation algorithms demonstrate thenecessity of comparative explanations and the effectiveness of our solution.

Comparative Explanations of Recommendations