Authors: A Okoso, K Otaki, S Koide, Y Baba
Year: 2025
Published in: ACM Transactions on Recommender Systems, 2025•dl.acm.org
Institution: Toyota Central R and D Labs, Toyota
Research Area: Human-Computer Interaction
Discipline: Machine Learning, Artificial Intelligence
The study demonstrates that tailoring the tone of textual explanations in recommender systems to domains and user attributes, such as age and personality traits, can enhance users' perceptions and engagement.
Methods: Two online user studies: (1) 470 participants evaluated synthetic explanations with six tones across three domains (movies, hotels, and home products), (2) 103 participants engaged with a real-world dataset from the hotel domain using a personalized recommender system.
Key Findings: The perceived effects of different textual explanation tones on users, examined across domains (movies, hotels, home products) and user attributes (e.g., age, personality traits).
DOI: https://dl.acm.org/doi/10.1145/3718101
Citations: 13
Sample Size: 573
