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Coherence conditions for preference modeling with ordered points

JOURNAL OF MATHEMATICAL PSYCHOLOGY 2017年 79卷 44-52页

作者： Ozturk, Meltem PSL Res Univ Univ Paris Dauphine CNRS F-75016 Paris France

In this article results on comparison rules on n ordered points are extended by introducing some coherence conditions. The main results concern a general characterization of coherence conditions and their influence on the 2-point, 3-point and 4-point interval representations of preference structures. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Decision-making preference modeling Intervals Coherence conditions

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Enriched preference modeling and robustness analysis for the ELECTRE Tri-B method

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ANNALS OF OPERATIONS RESEARCH 2021年第1-2期306卷 173-207页

作者： Kadzinski, Milosz Martyn, Magdalena Poznan Univ Tech Inst Comp Sci Piotrowo 2 PL-60965 Poznan Poland

We consider multiple criteria sorting problems with preference-ordered classes delimited by a set of boundary profiles. While significantly extending the ELECTRE Tri-B method, we present an integrated framework for modeling indirect preference information and conducting robustness analysis. We allow the Decision Maker (DM) to provide the following three types of holistic judgments: assignment examples, assignment-based pairwise comparisons, and desired class cardinalities. A diversity of recommendation that can be obtained given the plurality of outranking-based sorting models compatible with the DM's preferences is quantified by means of six types of results. These include possible assignments, class acceptability indices, necessary assignment-based preference relation, assignment-based outranking indices, extreme class cardinalities, and class cardinality indices. We discuss the impact of preference information on the derived outcomes, the interrelations between the exact results computed with mathematical programming and stochastic indices estimated with the Monte Carlo simulations, and new measures for quantifying the robustness of results. The practical usefulness of the approach is illustrated on data from the Financial Times concerning MBA programs.

关键词： Multiple criteria decision aiding Multiple criteria sorting Outranking relation preference modeling Robustness analysis Monte Carlo simulation

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A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

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COMPLEX & INTELLIGENT SYSTEMS 2017年第4期3卷 233-245页

作者： Wang, Handing Olhofer, Markus Jin, Yaochu Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Honda Res Inst Europe D-63073 Offenbach Germany

Evolutionary multi-objective optimization aims to provide a representative subset of the Pareto front to decision makers. In practice, however, decision makers are usually interested in only a particular part of the Pareto front of the multi-objective optimization problem. This is particularly true when the number of objectives becomes large. Over the past decade, preference-based multi-objective optimization has attracted increasing attention from both academia and industry due to its significance in both theory and practice. Significant progress has been made in evolutionary multi-objective optimization and multi-criteria decision communities, although many open issues still remain to be addressed. This paper provides a concise review on preference-based multi-objective optimization, including various preference modeling methods and existing preference-based optimization methods, as well as a brief discussion of the main future challenges.

关键词： Multi-objective optimization preference modeling preference learning

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Personality and preference modeling for Adaptive Storytelling 17

Personality and Preference Modeling for Adaptive Storytellin...

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Joint Conference on 17th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames) / 31st Conference on Graphics, Patterns and Images (SIBGRAPI) / 20th Symposium on Virtual and Augmented Reality (SVR)

作者： de Lima, Edirlei Soares Feijo, Bruno Furtado, Antonio L. Gottin, Vinicius M. Univ Europeia Sch Technol Arts & Commun Lisbon Portugal Pontifical Catholic Univ Rio De Janeiro Dept Informat Rio De Janeiro Brazil

ISBN: (纸本)9781538677698

In almost all forms of storytelling, the background and the current state of mind of the audience members predispose them to experience a given story from a unique personal perspective. However, traditional story writers usually construct their narratives based on an average understanding of the preferences of their audience, which does not guarantee satisfying narrative experiences for its individual members. When a narrative is aimed at providing pleasurable entertainment, having some information about the preferences of the current user for the narrative's content is vital to create satisfying experiences. This paper explores personality modeling and proposes a novel approach to generate individualized interactive narratives based on the preferences of users, which are modeled in terms of the Big Five factors. The paper presents the proposed method and evaluates its precision and real-time performance.

关键词： interactive storytelling personality modeling preference modeling

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Privacy preference modeling and Prediction in a Simulated Campuswide IoT Environment

Privacy Preference Modeling and Prediction in a Simulated Ca...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Lee, Hosub Kobsa, Alfred Univ Calif Irvine Donald Bren Sch Informat & Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9781509043279

With the advent of the Internet of Things (IoT), users are more likely to have privacy concerns since their personal information could be collected, analyzed, and utilized without notice by the networked IoT devices and services. Users may want to control all such activities by explicitly expressing their privacy preferences. However, it is becoming increasingly difficult for users to do so, not only because of the cognitive burden of continuously making privacy decisions for IoT services, but also because IoT devices have no, or only very restricted, user interfaces. Intelligent software helping users make better privacy decisions will be an important component of privacy preserving IoT environments. In order to construct such a component, we aim to verify whether it will be possible to computationally model and predict users' privacy preferences in IoT. To that end, we survey 172 participants in a simulated campuswide IoT environment about their privacy preferences regarding hypothetical personal information tracking scenarios. Then, we cluster the scenarios based on the survey responses, arriving at four clusters with distinct associated privacy preferences. Based on the clustering results, we uncover contextual factors that induce privacy violations in IoT. Finally, we build machine learning models to predict users' privacy decisions, using both contextual information and the corresponding cluster membership as training data. The final trained model shows 77% accuracy in predicting users' decisions whether or not to allow the respective IoT scenario.

关键词： IoT privacy preference modeling experience sampling Google Glass K-modes clustering conditional inference tree

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Stability-aware preference modeling for Sequential Recommendation

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ACM Transactions on the Web 1000年

作者： Chaoyong Wei Jiang Wenjun Kenli Li Jie Wu Hunan University Changsha China College of Information Science and Engineering Hunan University Changsha China CIS Temple University Philadelphia United States

Many researchers primarily rely on modeling user interests for sequential recommendation. However, dynamic user behaviors often accompany unstable interaction histories, and modeling user interests alone is insufficient for comprehensive user features. Some studies notice the problem of interest drift, but they are usually limited to modeling at the item-level, unable to perceive the subtle changes at the feature-level. To this end, we propose a Stability-aware preference Model (SAPM), which consists of three modules. The LSI module for extracting long and short-term interests, the FLC module for extracting feature-level candidate information, and the SAF module for fusing them according to the stability score. In particular, we propose a Multi-head GRU (MHGRU) structure in the LSI module, which is more efficient than the general GRU and has stronger expression ability. Through extensive experiments, our framework shows significant mitigation of the impact of unstable interactions. On the two real data sets, we improve MRR by 5.7% and 14.0% compared with the recent baselines. Moreover, we conduct an in-depth analysis of user interaction stability and obtain several interesting findings that can benefit future studies.

关键词： sequential recommendation preference modeling stability-aware

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Identical Human preference Alignment Paradigm for Text-to-Image Models

Identical Human Preference Alignment Paradigm for Text-to-Im...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sun, Haoyuan Xia, Bo Zhao, Yifei Chang, Yongzhe Wang, Xueqian Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798350368741

Implicit reward mechanism of Direct preference Optimization (DPO) has facilitated its recent applications beyond large language models (LLMs), notably in aligning text-to-image models with human preferences. While promising results have been achieved with algorithms such as Diffusion-DPO, their reliance on the assumptions of the Bradley-Terry model could potentially lead to significant overfitting. In this paper, we propose the Step Identical preference Alignment (SIPA) method, departing text-to-image alignment from the assumptions of Bradley-Terry preference model. We assess the performance of four models, Diffusion-DPO, SPO, and SIPA, alongside the original model, on the HPS-V2 test set, which focus on three key aspects: text-image alignment, human value alignment, and generation diversity. Experimental results show that SIPA matches or outperforms existing SOTA alignment methods, and even exceeds the original model in terms of generation diversity, which compellingly demonstrates SIPA's superiority in mitigating alignment overfitting. © 2025 IEEE.

关键词： human preference alignment preference modeling text-to-image model

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Competition-driven robust multicriteria ranking for managing interactive generalized orthopair information in humanitarian operations

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INFORMATION SCIENCES 2025年 700卷

作者： Zhang, Wenkai Gao, Hengxia Guo, Haixiang Pamucar, Dragan China Univ Geosci Sch Econ & Management Beijing 100083 Peoples R China Beijing Wuzi Univ Sch Logist Beijing 101149 Peoples R China China Univ Geosci Sch Econ & Management Wuhan 430078 Peoples R China Vilnius Gediminas Tech Univ Transport & Logist Competence Ctr Vilnius Lithuania

Multicriteria decision-making (MCDM) provides a structured framework to support decision- making in humanitarian organizations, particularly by considering the evaluations and preferences of decision makers in the form of generalized orthopair fuzzy sets. To address the challenges posed by interactive generalized orthopair MCDM problems involving competition and higher- order uncertainty, a competition-driven robust multicriteria ranking approach based on a three-stage solution framework is proposed. Firstly, two kinds of game cross-closeness models based on benevolent and aggressive competition strategies are developed to determine the optimal capacities characterizing interactions among criteria. Building upon this, we propose a competition-driven Choquet decision method that incorporates a set of novel operational laws for generalized orthopair fuzzy sets, which effectively addresses potential authoritarian behavior. Subsequently, an interactive robust ranking approach, grounded in stochastic multicriteria acceptability analysis, is presented to address the simultaneous uncertainties inherent in decision models and model inputs, allowing for competition among alternatives. Finally, the effectiveness and advantages of the proposed robust multicriteria ranking approach are demonstrated through a case study, a series of experiments, and a comprehensive comparative analysis. The results demonstrate that the proposed approach not only provides robust recommendations but also excels in effectively managing competing alternatives while reducing cognitive burden on decision makers.

关键词： Multicriteria decision-making Q-rung orthopair fuzzy sets Robust ranking Choquet integral preference modeling

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Fifty years of multiple criteria decision analysis: From classical methods to robust ordinal regression

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第2期323卷 351-377页

作者： Greco, Salvatore Slowinski, Roman Wallenius, Jyrki Univ Catania Dept Econ & Business I-95129 Catania Italy Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland Aalto Univ Sch Business POB 11000 Aalto 00076 Finland

Multiple Criteria Decision Analysis (MCDA) is a subfield of Operational Research that aims to support Decision- Makers (DMs) in the decision-making process through mathematical models and computational procedures. In this perspective, MCDA employs structured and traceable protocols to identify potential actions and the criteria for evaluating them. MCDA procedures aim to define recommendations consistent with the preferences of DMs for the specific decision problem at hand. These problems are generally formulated in terms of either choosing the best action, classifying actions into pre-defined and ordered decision classes, or ranking actions from best to worst. As the evaluation criteria are generally conflicting, the main challenge is to aggregate them into a mathematical preference model representing the DM value system. We review the development of MCDA over the past fifty years and describe its evolution with examples of distinctive methods. They are distinguished by the type of preference information elicited by DMs, the type of the preference model (criteria aggregation), and the way of converting the preference relation induced by the preference model in the set of potential actions into a decision recommendation. We focus on MCDA methods with a finite set of actions. References to specific application areas will be given. In the conclusion section, some prospective avenues of research will be outlined.

关键词： Multiple criteria decision analysis preference modeling Best choice Ordinal classification Ranking

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Separating and Learning Latent Confounders to Enhancing User preferences modeling 29th

Separating and Learning Latent Confounders to Enhancing User...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Xu, Hangtong Xu, Yuanbo Yang, Yongjia Jilin Univ Coll Comp Sci & Technol MIC Lab Changchun Peoples R China

ISBN: (纸本)9789819755547;9789819755554

Recommender models aim to capture user preferences from historical feedback and then predict user-specific feedback on candidate items. However, the presence of various unmeasured confounders causes deviations between the user preferences in the historical feedback and the true preferences, resulting in models not meeting their expected performance. Existing debias models either (1) specific to solving one particular bias or (2) directly obtain auxiliary information from user historical feedback, which cannot identify whether the learned preferences are true user preferences or mixed with unmeasured confounders. Moreover, we find that the former recommender system is not only a successor to unmeasured confounders but also acts as an unmeasured confounder affecting user preference modeling, which has always been neglected in previous studies. To this end, we incorporate the effect of the former recommender system and treat it as a proxy for all unmeasured confounders. We propose a novel framework, Separating and Learning Latent Confounders For Recommendation (SLFR), which obtains the representation of unmeasured confounders to identify the counterfactual feedback by disentangling user preferences and unmeasured confounders, then guides the target model to capture the true preferences of users. Extensive experiments in five real-world datasets validate the advantages of our method.

关键词： Recommender system Debiasing Causal inference preference modeling

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