helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How do users attribute recommendation success or failure, and how do these attributions affect system trust?AI Explainability, Trust, and Calibration / Explanation, Control, and Trust in Recommendation Systems
How do users attribute recommendation success or failure, and how do these attributions affect system trust?
Similar questions
helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Can ChatGPT-generated recommendation explanations outperform random explanations in personalization and persuasiveness?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How do personalized explanations based on user preferences differ from generic explanations in perceived user effectiveness in recommendation systems?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
For unknown recommended items, which explanation types better improve user acceptance and trust?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Does adolescent users' trust in educational recommendation systems increase with slider controls over recommendation content difficulty?IUI '23Steering Recommendations and Visualising Its Impact: Effects on Adolescents’ Trust in E-Learning Platforms
helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Can interfaces visualizing control effects enhance adolescents' transparency and trust in educational recommendation systems?IUI '23Steering Recommendations and Visualising Its Impact: Effects on Adolescents’ Trust in E-Learning Platforms
helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How can control and visualization mechanisms suitable for adolescents be designed in educational recommendation systems to balance user adjustment and recommendation efficiency?IUI '23Steering Recommendations and Visualising Its Impact: Effects on Adolescents’ Trust in E-Learning Platforms
Related papers
IUI 2024
Leveraging ChatGPT for Automated Human-centered Explanations in Recommender Systems
Ítallo Silva, Leandro Marinho, Alan Said
CHI 2023
When Recommender Systems Snoop into Social Media, Users Trust them Less for Health Advice
Yuan Sun, Magdalayna Drivas, Mengqi Liao
IUI 2022
Similarity-Based Explanations meet Matrix Factorization via Structure-Preserving Embeddings
Leandro Balby Marinho, Júlio Guedes, Denis Parra
UIST 2022
Summarizing Sets of Related ML-Driven Recommendations for Improving File Management in Cloud Storage
Will Brackenbury, Kyle Chard, Aaron Elmore
CHI 2022
User Trust in Recommendation Systems: A comparison of Content-Based, Collaborative and Demographic Filtering
Mengqi Liao, S. Shyam Sundar, Joseph B. Walther
CHI 2022
Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems
Wanling Cai, Yucheng Jin, Li Chen
CHI 2022
From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks
Hyeonsu B Kang, Rafal Kocielnik, Andrew Head
CHI 2021
Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization Recommendations
Rachael Zehrung, Astha Singhal, Michael Correll
CHI 2021
The Effects of Warmth and Competence Perceptions on Users' Choice of an AI System
Zohar Gilad, Ofra Amir, Liat Levontin