PREF-XAI treats explanations as ranked alternatives and learns additive utility functions from limited user feedback to select and discover personalized rule explanations for black-box models.
Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization , pages =
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Humanities scholars require recommender user models for digital archives that account for context volatility, epistemic trust, contrastive seeking, and strand continuity instead of stable preferences and session-bounded interactions.
citing papers explorer
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PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models
PREF-XAI treats explanations as ranked alternatives and learns additive utility functions from limited user feedback to select and discover personalized rule explanations for black-box models.
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What Do Humanities Scholars Need? A User Model for Recommendation in Digital Archives
Humanities scholars require recommender user models for digital archives that account for context volatility, epistemic trust, contrastive seeking, and strand continuity instead of stable preferences and session-bounded interactions.
- iPOE: Interpretable Prompt Optimization via Explanations