Comparative Explanations of Recommendations
Comparative Explanations of Recommendations
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DOI：
10.1145/3485447.3512031
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Aobo Yang;Nan Wang;Renqin Cai;Hongbo Deng;Hongning Wang
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文献类型：
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作者：
Aobo Yang;Nan Wang;Renqin Cai;Hongbo Deng;Hongning Wang
由于推荐本质上是一个比较（或排名）过程，因此一个好的解释应该向用户说明为什么一个项目被认为比另一个项目更好，即，对推荐项目的比较说明。理想情况下，在阅读解释之后，用户应该达到与系统相同的项目排名。不幸的是，很少有研究注意到这种比较的解释。在这项工作中，我们开发了一个提取和细化架构来解释一组排名项目从推荐系统之间的相对比较。对于每个推荐项目，我们首先从相关评论中提取一个句子，最适合与一组参考项目进行所需的比较。然后，通过生成模型将提取的句子进一步与目标用户进行表达，以更好地解释推荐该项目的原因。我们设计了一个新的解释质量指标的基础上BLEU指导提取和细化组件的端到端的训练，这避免了通用内容的生成。对两个大型推荐基准数据集和一系列最先进的可解释推荐算法的认真用户研究的广泛离线评估表明了比较解释的必要性和我们解决方案的有效性。
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.