The thesis identifies theoretical, empirical, and conceptual flaws in offline fairness measures for recommender systems and contributes new evaluation methods and practical guidelines.
Pazzani and Daniel Billsus.Content-Based Recommendation Sys- tems, pages 325–341
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Personalized deep learning models on multimodal physiological signals from an Empatica E4 sensor achieve 92.68% accuracy for driver state classification in real-world automated driving, compared to 54% for generalized models across four drivers.
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Offline Evaluation Measures of Fairness in Recommender Systems
The thesis identifies theoretical, empirical, and conceptual flaws in offline fairness measures for recommender systems and contributes new evaluation methods and practical guidelines.
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Human Centered Non Intrusive Driver State Modeling Using Personalized Physiological Signals in Real World Automated Driving
Personalized deep learning models on multimodal physiological signals from an Empatica E4 sensor achieve 92.68% accuracy for driver state classification in real-world automated driving, compared to 54% for generalized models across four drivers.