Recommender systems assist users in decision-making, where the presentation of recommended items and their explanations are critical factors for enhancing the overall user experience. Although various methods for generating explanations have been proposed, there is still room for improvement, particularly for users who lack expertise in a specific item domain. In this study, we introduce the novel concept of \textit{consequence-based explanations}, a type of explanation that emphasizes the individual impact of consuming a recommended item on the user, which makes the effect of following recommendations clearer. We conducted an online user study to examine our assumption about the appreciation of consequence-based explanations and their impacts on different explanation aims in recommender systems. Our findings highlight the importance of consequence-based explanations, which were well-received by users and effectively improved user satisfaction in recommender systems. These results provide valuable insights for designing engaging explanations that can enhance the overall user experience in decision-making.

翻译：推荐系统辅助用户进行决策，其中推荐项目的呈现及其解释是提升整体用户体验的关键因素。尽管已有多种生成解释的方法被提出，但仍存在改进空间，尤其是在用户对特定项目领域缺乏专业知识的情况下。在本研究中，我们引入了新颖的“基于后果的解释”概念。这种解释强调消费推荐项目对用户的个体影响，从而使得遵循推荐的后果更加清晰。我们开展了一项在线用户研究，以验证关于用户对基于后果解释的偏好及其对推荐系统中不同解释目标影响的假设。研究结果凸显了基于后果解释的重要性，这类解释受到用户好评，并有效提升了推荐系统中的用户满意度。这些结论为设计引人入胜的解释提供了宝贵见解，有助于增强用户在决策过程中的整体体验。