%0 Conference Paper %1 Okoso2024 %A Okoso, Ayano %A Otaki, Keisuke %A Koide, Satoshi %A Baba, Yukino %B Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization %C New York, NY, USA %D 2024 %I Association for Computing Machinery %K adaptive-presentation explanations recommender umap2024 %P 261–266 %R 10.1145/3627043.3659572 %T Toward Tone-Aware Explanations in Recommender Systems %U https://doi.org/10.1145/3627043.3659572 %X 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 (e.g., transparency) 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 tonal effects of explanations through an online user study. We focus on a hotel domain and six types of tones. The collected data analysis reveals that the tone of explanations influences the perceived effects, such as trust and effectiveness, of recommender systems. Our findings suggest that the tone of explanations can enhance user experience in recommender systems. %@ 9798400704338

@inproceedings{Okoso2024, 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 (e.g., transparency) 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 tonal effects of explanations through an online user study. We focus on a hotel domain and six types of tones. The collected data analysis reveals that the tone of explanations influences the perceived effects, such as trust and effectiveness, of recommender systems. Our findings suggest that the tone of explanations can enhance user experience in recommender systems.}, added-at = {2024-07-01T17:03:01.000+0200}, address = {New York, NY, USA}, author = {Okoso, Ayano and Otaki, Keisuke and Koide, Satoshi and Baba, Yukino}, biburl = {https://www.bibsonomy.org/bibtex/24ee267d882424161b4114c2a7f02a273/brusilovsky}, booktitle = {Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization}, day = 22, description = {Toward Tone-Aware Explanations in Recommender Systems | Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3627043.3659572}, interhash = {844264f701254398b909cb85be161f82}, intrahash = {4ee267d882424161b4114c2a7f02a273}, isbn = {9798400704338}, keywords = {adaptive-presentation explanations recommender umap2024}, location = {Cagliari, Italy}, month = {6}, pages = {261–266}, publisher = {Association for Computing Machinery}, series = {UMAP '24}, timestamp = {2024-07-01T17:03:01.000+0200}, title = {Toward Tone-Aware Explanations in Recommender Systems}, url = {https://doi.org/10.1145/3627043.3659572}, year = 2024 }