Providing natural language explanations for recommendations is particularly useful from the perspective of a non-expert user. Although several methods for providing such explanations have recently been proposed, we argue that an important aspect of explanation quality has been overlooked in their experimental evaluation. Specifically, the coherence between generated text and predicted rating, which is a necessary condition for an explanation to be useful, is not properly captured by currently used evaluation measures. In this paper, we highlight the issue of explanation and prediction coherence by 1) presenting results from a manual verification of explanations generated by one of the state-of-the-art approaches 2) proposing a method of automatic coherence evaluation 3) introducing a new transformer-based method that aims to produce more coherent explanations than the state-of-the-art approaches 4) performing an experimental evaluation which demonstrates that this method significantly improves the explanation coherence without affecting the other aspects of recommendation performance.

翻译：为推荐系统提供自然语言解释，对于非专业用户而言尤为实用。尽管近期已有多种提供此类解释的方法被提出，但我们认为其实验评估中一个重要方面——解释质量——被忽视了。具体而言，生成的文本与预测评分之间的连贯性（这是解释有用性的必要条件）未被当前使用的评估指标恰当捕捉。本文通过以下方式凸显解释与预测的连贯性问题：1) 呈现对某最新方法生成的解释进行人工验证的结果；2) 提出一种自动连贯性评估方法；3) 引入一种基于Transformer的新方法，旨在生成比现有方法更连贯的解释；4) 进行实验评估，证明该方法能在不影响推荐性能其他方面的前提下显著提升解释的连贯性。