  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.
