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.
翻译:由于建议基本上是一个比较(或排名)过程,因此应该向用户说明为什么一个项目被认为比另一个项目好,即对建议的项目作比较解释。理想的情况是,在阅读解释之后,用户应该达到与系统项目相同的排名。不幸的是,这种比较解释没有引起多少研究注意。在这项工作中,我们开发了一个抽查和检索结构来解释一组建议系统排名项目之间的相对比较。对于每个建议项目,我们首先从其相关的审查中抽出一句话,最适合于一套参考项目的预期比较。然后,通过一个基因化模型,进一步阐述针对目标用户的这一摘录句,以更好地解释为什么建议该项目。我们根据BLEU设计了一个新的解释质量指标,以指导抽取和精炼部件的端到端培训,避免产生通用内容。对两个大型建议基准数据集进行广泛的离线评价,并根据一系列最新可解释的建议算法进行认真的用户研究,表明比较解释的必要性和我们解决办法的有效性。