A non-Bayesian preference elicitation approach for noisy decision models
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Full Text E-thesis
Date
2025
Authors
Pourkhajouei, Samira
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Publisher
University College Cork
Published Version
Abstract
Interactive preference elicitation plays a crucial role in multi-criteria decisionmaking (MCDM) and recommender systems, particularly when user preferences are uncertain or noisy. Traditional Bayesian approaches offer a principled way to handle such uncertainty by representing preferences as probabilistic models, updating them iteratively based on user responses. However, these methods often entail high computational costs and require prior assumptions on preference distributions, making them less feasible for real-time applications.
This thesis introduces a non-Bayesian framework for interactive preference elicitation under noisy user responses, offering a computationally efficient alternative to probabilistic approaches. Instead of relying on prior distributions, our method identifies and refines a set of plausible preference models by evaluating their consistency with observed user responses. The framework incrementally selects targeted queries that maximize information gain while remaining robust to response inconsistencies, allowing for an effective tradeoff between query efficiency and recommendation accuracy.
We propose a novel query selection strategy that prioritizes alternatives based on their potential optimality across multiple preference models, reducing the number of interactions required to identify the user’s most preferred alternative. To ensure scalability, our method operates on a finite approximation of the preference space, allowing for efficient real-time decision-making.
Empirical evaluations demonstrate the effectiveness of the proposed approach, showing that it achieves high accuracy in identifying user preferences while significantly reducing computational costs compared to Bayesian and minimax regret-based methods. The results suggest that this non-Bayesian framework is well-suited for interactive decision-support systems, personalized recommendations, and real-time MCDM applications, offering a practical balance between efficiency, adaptability, and robustness to noise.
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Keywords
Preference elicitation , Preference learning , Decision-making , User preference models
Citation
Pourkhajouei, S. 2025. A non-Bayesian preference elicitation approach for noisy decision models. MSc Thesis, University College Cork.