Impact of Tone-Aware Explanations in Recommender Systems

TL;DR

This work investigates how the expressiveness of explanation tone in recommender systems influences user perception. Using an online study with LLM-generated fictional items across movies, hotels, and home products, the authors employ pairwise comparisons and ordinal logistic regression to quantify tone effects on eight perception metrics, uncovering domain-specific and user-attribute–dependent patterns. They find that richer expressive tones generally enhance perceived effects, with hotel and practical domains showing stronger sensitivity than entertainment domains, and that age and personality traits modulate these effects. The study highlights the practical significance of tailoring explanation tone to domain and user characteristics to improve user experience, while also noting potential risks of overestimating recommendations due to tone. Overall, the findings motivate developing predictive methods to select effective tones for individual users and contexts.

Abstract

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.