Toward Tone-Aware Explanations in Recommender Systems
Pages 261 - 266
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.
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Index Terms
- Toward Tone-Aware Explanations in Recommender Systems
Recommendations
LLM-generated Explanations for Recommender Systems
UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and PersonalizationUsers are often confronted with situations where they have to decide in favor or against an offered item, like a book, movie, or recipe. Those suggested items are commonly determined by a recommender system, which considers personal preferences to ...
The Reason Why: A Survey of Explanations for Recommender Systems
Adaptive Multimedia Retrieval: Semantics, Context, and AdaptationAbstractRecommender Systems refer to those applications that offer contents or items to the users, based on their previous activity. These systems are broadly used in several fields and applications, being common that an user interact with several ...
Knowledgeable Explanations for Recommender Systems
WI-IAT '10: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01Recommender Systems (RS) serve online customers in identifying those items from a variety of choices that best match their needs and preferences. In this context explanations summarize the reasons why a specific item is proposed and strongly increase ...
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Published In
June 2024
338 pages
ISBN:9798400704338
DOI:10.1145/3627043
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Published: 22 June 2024
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UMAP '24: 32nd ACM Conference on User Modeling, Adaptation and Personalization
July 1 - 4, 2024
Cagliari, Italy
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