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AI Explainability, Trust, and Calibration / Explanation, Control, and Trust in Recommendation Systems

Adolescents struggle to understand and trust algorithmically recommended learning content.

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

Similar questions

lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems

Users lack trust in recommendation system content and do not understand recommendation rationales.

IUI '24Leveraging ChatGPT for Automated Human-centered Explanations in Recommender Systems

lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems

Users have a trust crisis toward health recommendation systems based on social media data.

CHI '23When Recommender Systems Snoop into Social Media, Users Trust them Less for Health Advice

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.

IUI '22Similarity-Based Explanations meet Matrix Factorization via Structure-Preserving Embeddings

lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems

Users face heavy burden managing recommended file operations one by one in cloud storage.

UIST '22Summarizing Sets of Related ML-Driven Recommendations for Improving File Management in Cloud Storage

lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems

Users have insufficient trust in recommendation systems, especially during cold-start phases.

CHI '22User Trust in Recommendation Systems: A comparison of Content-Based, Collaborative and Demographic Filtering

lightbulbPractical problemExplanation, Control, and Trust in Recommendation Systems

Insufficient user trust in conversational recommender systems leads to low adoption.

CHI '22Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems

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Leveraging ChatGPT for Automated Human-centered Explanations in Recommender Systems

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When Recommender Systems Snoop into Social Media, Users Trust them Less for Health Advice

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Similarity-Based Explanations meet Matrix Factorization via Structure-Preserving Embeddings

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Summarizing Sets of Related ML-Driven Recommendations for Improving File Management in Cloud Storage

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User Trust in Recommendation Systems: A comparison of Content-Based, Collaborative and Demographic Filtering

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Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems

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From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks

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Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization Recommendations

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