Impact of Tone-Aware Explanations in Recommender Systems

TL;DR

This paper explores how the tone of explanations in recommender systems, such as formal or humorous styles, affects user perception and decision-making across different domains and user attributes.

Contribution

It introduces a large language model-generated dataset to analyze the impact of explanation tone in recommender systems across multiple domains and user characteristics.

Findings

Tone effects vary by domain (movies, hotels, home products).

User attributes like age and personality influence tone perception.

Tone can enhance user experience in recommender explanations.

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.