A non-Bayesian preference elicitation approach for noisy decision models

Simple item page
dc.contributor.advisorWilson, Nic
dc.contributor.advisorPrestwich, Steve
dc.contributor.authorPourkhajouei, Samira
dc.contributor.funderScience Foundation Ireland
dc.contributor.funderInsight SFI Research Centre for Data Analytics
dc.date.accessioned2025-10-01T15:05:35Z
dc.date.available2025-10-01T15:05:35Z
dc.date.issued2025
dc.date.submitted2025
dc.description.abstractInteractive 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.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationPourkhajouei, S. 2025. A non-Bayesian preference elicitation approach for noisy decision models. MSc Thesis, University College Cork.
dc.identifier.endpage96
dc.identifier.urihttps://hdl.handle.net/10468/17935
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/12/RC/2289_P2/IE/INSIGHT_Phase 2 /
dc.rights© 2025, Samira Pourkhajouei.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectPreference elicitation
dc.subjectPreference learning
dc.subjectDecision-making
dc.subjectUser preference models
dc.titleA non-Bayesian preference elicitation approach for noisy decision models
dc.typeMasters thesis (Research)en
dc.type.qualificationlevelMastersen
dc.type.qualificationnameMSc - Master of Scienceen
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
PourkhajoueiS_MSc2025.pdf
Size:
697.14 KB
Format:
Adobe Portable Document Format
Description:
Full Text E-thesis
Download
Thumbnail Image
Name:
3. 120224105 - Samira Pourkhajouei - Submission for Examination Form.pdf
Size:
1.27 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
5.2 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Research Theses
Computer Science - Masters by Research Theses
Insight Centre for Data Analytics - Masters by Research Theses