In recommender systems, the presentation of explanations plays a crucial role in supporting users' decision-making processes. Although numerous existing studies have focused on the effects (transparency or persuasiveness) of explanation content, explanation expression is largely overlooked. Tone, such as formal and humorous, is directly linked to expressiveness and is an important element in human communication. However, studies on the impact of tone on explanations within the context of recommender systems are insufficient. Therefore, this study investigates the effect of explanation tones through an online user study from three aspects: perceived effects, domain differences, and user attributes. We create a dataset using a large language model to generate fictional items and explanations with various tones in the domain of movies, hotels, and home products. Collected data analysis reveals different perceived effects of tones depending on the domains. Moreover, user attributes such as age and personality traits are found to influence the impact of tone. This research underscores the critical role of tones in explanations within recommender systems, suggesting that attention to tone can enhance user experience.

翻译：在推荐系统中，解释的呈现方式在支持用户决策过程中起着至关重要的作用。尽管已有大量研究聚焦于解释内容的效果（如透明性或说服力），但解释的表达方式在很大程度上被忽视。语气（如正式与幽默）与表达力直接相关，是人类交流中的重要元素。然而，关于语气在推荐系统解释中的影响研究尚不充分。因此，本研究通过在线用户实验，从感知效果、领域差异和用户属性三个方面探讨解释语气的影响。我们利用大语言模型生成包含电影、酒店和家居产品领域虚构物品及其多语气解释的数据集。数据分析表明，不同领域中语气的感知效果存在差异。此外，用户属性（如年龄和人格特质）也会影响语气的作用。本研究强调了语气在推荐系统解释中的关键作用，提示关注语气能够增强用户体验。