  As recommendation is essentially a comparative (or ranking) process, a good
explanation should illustrate to users why an item is believed to be better
than another, i.e., comparative explanations about the recommended items.
Ideally, after reading the explanations, a user should reach the same ranking
of items as the system's. Unfortunately, little research attention has yet been
paid on such comparative explanations.
  In this work, we develop an extract-and-refine architecture to explain the
relative comparisons among a set of ranked items from a recommender system. For
each recommended item, we first extract one sentence from its associated
reviews that best suits the desired comparison against a set of reference
items. Then this extracted sentence is further articulated with respect to the
target user through a generative model to better explain why the item is
recommended. We design a new explanation quality metric based on BLEU to guide
the end-to-end training of the extraction and refinement components, which
avoids generation of generic content. Extensive offline evaluations on two
large recommendation benchmark datasets and serious user studies against an
array of state-of-the-art explainable recommendation algorithms demonstrate the
necessity of comparative explanations and the effectiveness of our solution.
