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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A survey that organizes fairness research in LLM-based recommender systems via a two-dimensional taxonomy of bias mechanisms and fairness targets while linking to other trustworthy AI concerns.
iPOE generates and optimizes annotation guidelines from explanations to produce interpretable prompts, reporting up to 39% gains over baselines on four datasets with LLM explanations substituting for human ones.
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.
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