helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Can ChatGPT-generated recommendation explanations outperform random explanations in personalization and persuasiveness?Direction: AI Explainability, Trust, and Calibration
Explanation, Control, and Trust in Recommendation Systems
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44 items
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?lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Users lack trust in recommendation system content and do not understand recommendation rationales.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
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
helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How do health recommendation systems based on social media data affect user trust?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Can letting users choose recommendation personalization methods reduce identity threat and enhance user trust?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
What psychological differences exist among personalization methods in privacy risk and identity threat?lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Users have a trust crisis toward health recommendation systems based on social media data.helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How can structure-preserving embeddings improve the similarity explainability of matrix factorization methods while maintaining recommendation performance?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Which frozen PCA principal component factors most effectively improve model explainability when optimizing embedding training dimensions?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Can structure-preserving embeddings significantly improve neighborhood overlap metrics in recommender systems while matching the predictive performance of complex deep learning methods?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.helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How can related machine learning-driven file recommendations be clustered and summarized to improve file management in cloud storage?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Which recommendation summary presentations (e.g., file lists, rule text, rule trees) improve user trust and operational efficiency with recommender systems?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How do rule-based recommendation summary algorithms balance generation efficiency and recommendation comprehensibility?lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Users face heavy burden managing recommended file operations one by one in cloud storage.helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How do content filtering, collaborative filtering, and demographic filtering recommendation systems differ in their effects on user trust?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Under cold-start conditions, how do different recommendation methods affect users' trust in the system?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How do users attribute recommendation success or failure, and how do these attributions affect system trust?lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Users have insufficient trust in recommendation systems, especially during cold-start phases.helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How do users' personal characteristics (e.g., personality traits, trust propensity, domain knowledge) affect trust relationships with conversational recommender systems (CRS)?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How do different system interaction strategies (user-led vs. mixed-led) differ in their effects on user trust?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How does task complexity moderate the relationship between user characteristics and system trust?lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Insufficient user trust in conversational recommender systems leads to low adoption.helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How can researchers efficiently filter and discover relevant literature in rapidly growing scientific corpora?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How can implicit social networks (e.g., author relations and citation networks) enhance explainability and user trust in scientific recommender systems?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Can indirect author-relation messages improve recommender system coverage while increasing user acceptance of recommended content?lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Researchers struggle to quickly filter valuable scientific literature, and recommender systems lack explainability.helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How can conversational explanation methods be designed in review-based recommender systems to support users' natural-language explanation requests?CUI '21Conversational Review-Based Explanations for Recommender Systems: Exploring Users’ Query Behavior
helpResearch questionExplanation, Control, and Trust in Recommendation Systems
What typical questions do users ask in conversational review-based recommendation explanation scenarios?CUI '21Conversational Review-Based Explanations for Recommender Systems: Exploring Users’ Query Behavior
helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Can conversational review-based recommendation explanations improve users' perceptions of system transparency, effectiveness, and trust?CUI '21Conversational Review-Based Explanations for Recommender Systems: Exploring Users’ Query Behavior
lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Users struggle to understand recommendation results through static review explanations due to insufficient interactivity.CUI '21Conversational Review-Based Explanations for Recommender Systems: Exploring Users’ Query Behavior
helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Do users trust human-generated visualization recommendations more than algorithm-generated ones?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Do recommendation source labels affect users' evaluations of visualization recommendation quality?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Which behavioral patterns influence users' decisions when choosing visualization recommendations?lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Non-expert users struggle to judge whether algorithm-recommended visualizations are reliable.helpResearch questionExplanation, Control, and Trust in Recommendation Systems
How do warmth (friendliness, trustworthiness) and competence (performance, accuracy) dimensions affect users' decisions when choosing AI systems?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
When AI systems present conflicting warmth and competence (e.g., high warmth/low competence), which type do users choose?helpResearch questionExplanation, Control, and Trust in Recommendation Systems
Do warmth and competence priorities differ across application domains?lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems
Users struggle to balance system performance and friendly image needs when selecting AI recommendation systems.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
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