Impact of Tone-Aware Explanations in Recommender Systems

Abstract

In recommender systems, explanations are essential for supporting users’ decision-making processes. While many studies have focused on explanation content or user interface, the expression of textual explanations has been largely overlooked. The expression refers to textual styles such as formal or humorous, which we call tone in this article. Although tone contributes to smooth human communication, its impact on users’ perceptions of recommender systems remains largely unexplored. In particular, it is unclear whether the perceived effects of explanation tone differ by domain or user attributes. Therefore, we investigate the effects of explanation tones through two online user studies considering domains and user attributes. In the first study with 470 participants, we generated datasets using a large language model to create fictional items and explanations with six tones across three domains: movies, hotels, and home products. The participants evaluated two explanations for an item, each presented in a different tone, and rated 10 metrics. In the second study with 103 participants, we used a real-world dataset from the hotel domain and incorporated a simple personalized recommender system to examine effects of tone in a more realistic setting. The results revealed that the perceived effects of tones differ by domain and are significantly influenced by user attributes such as age and personality traits. Our findings suggest that appropriately adjusting the tone of explanations according to domains and user attributes can enhance the perceived effects of recommender systems.