lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Users have insufficient trust in recommendation systems, especially during cold-start phases.AI Explainability, Trust, and Calibration / Explanation, Control, and Trust in Recommendation Systems
Users have insufficient trust in recommendation systems, especially during cold-start phases.
Similar questions
lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Users lack trust in recommendation system content and do not understand recommendation rationales.lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Adolescents struggle to understand and trust algorithmically recommended learning content.IUI '23Steering Recommendations and Visualising Its Impact: Effects on Adolescents’ Trust in E-Learning Platforms
lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Users have a trust crisis toward health recommendation systems based on social media data.lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Users do not trust recommendation results because the embedding models used by recommender systems are too difficult to explain.lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Users face heavy burden managing recommended file operations one by one in cloud storage.lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Insufficient user trust in conversational recommender systems leads to low adoption.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