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A review on fuzzy preference modeling methods for group decision-making

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期40卷 10645-10660页

作者： Wang, Guan Wu, Lingjiu Liu, Yusheng Ye, Xiaoping Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China Xian Satellite Control Ctr Xian Peoples R China Lishui Univ Sch Engn Lishui Peoples R China

With the rise of group decision-making and the increasingly complex decision-making environment, preference modeling for decision makers has become more and more important, and many preference modeling methods have emerged. Based on the fuzzy theory, researchers have proposed a large number of preference models to express the subjective uncertainty of decision makers. These methods based on fuzzy theory are collectively referred to as fuzzy preference modeling methods. The fuzzy sets preference model is the first practice of fuzzy theory used in the field of preference modeling, and it is still widely used by researchers until now. Subsequently, based on fuzzy theory, the researchers also proposed linguistic term sets and cloud model. These methods have different representation domains, and are applicable to different decision-making environment. In this paper we give a review of classical fuzzy preference modeling methods and its latest extensions and variants. After the presentation of comparative analyses on the existing methods, we figure out some current challenges and possible future development directions in the field of fuzzy preference modeling.

关键词： preference modeling fuzzy preference modeling fuzzy sets group decision-making decision-making modeling

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Hierarchical temporal-spatial preference modeling for user consumption location prediction in Geo-Social Networks

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INFORMATION PROCESSING & MANAGEMENT 2021年第6期58卷 102715-102715页

作者： Xu, Shuai Pi, Dechang Cao, Jiuxin Fu, Xiaoming Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Univ Gottingen Inst Comp Sci Gottingen Germany

Predicting where people will consume in the future is of great significance for promoting local business. Although the prevalence of Geo-Social Networks (GSNs) has provided sufficient and desirable geo-tagged data for user mobility modeling, most studies attempt to directly fit user's preference toward locations through exploring the complex interaction between ⟨user, location⟩ pairs, which is usually hard to incorporate temporal-spatial context and side information. Moreover, the availability of multi-modal data associated with both user and location in GSNs has not yet been comprehensively leveraged. In view of the above-mentioned situations, in this article, we propose a two-stage framework composed of a Temporal Base Model (TBM) and a Location Prediction Model (LPM) to accomplish the task of user consumption location prediction at a given time in the future. In the first stage, based on user sentimental textual reviews, we leverage the hierarchical attention mechanism to capture time-sensitive user latent preference. In the second stage, we fuse the multifaceted context to derive the user's consumption probability toward different locations at the given time. We conduct extensive experiments over three real-world GSN datasets to verify the performance of the proposed approach. The experimental results encouragingly demonstrate the effectiveness of the two-stage framework, which outperforms multiple baselines in terms of different evaluation metrics such as accuracy, average percentile rank (APR) and coverage ratio.

关键词： Location prediction preference modeling Hierarchical attention Feature fusion Geo-social networks

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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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Adaptive storytelling based on personality and preference modeling

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ENTERTAINMENT COMPUTING 2020年 34卷 100342-100342页

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

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 uniquely personal perspective. However, traditional story writers usually construct their narratives based on the average preferences of their audience, which does not guarantee satisfying narrative experiences for its members. When a narrative aims at providing pleasurable entertainment, having some information about the preferences of the current user for the narratives 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 we model in terms of the Big Five factors. This paper presents and evaluates the proposed method in a web-based interactive storytelling system that explores the Little Red Riding Hood folktale. The results show that the proposed method is capable of correctly recognizing the preferences of users for story events (average accuracy of 91.9%) and positively improve user satisfaction and experience.

关键词： Interactive storytelling Personality modeling preference modeling

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preference modeling Tools for the Decision Support Method ELECTRE-III-H 22

Preference Modeling Tools for the Decision Support Method EL...

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22nd International Conference of the Catalan-Association-for-Artificial-Intelligence (CCIA)

作者： Valls, Aida Moreno, Antonio Pascual-Fontanilles, Jordi Univ Rovira & Virgili Dept Engn Informat & Matemat Avda Paisos Catalans 26 Tarragona 43007 Catalonia Spain

ISBN: (纸本)9781643680156;9781643680149

ELECTRE is a multi-criteria decision aiding methodology widely extended in many fields of application, such as environmental risk assessment, systems evaluation or analysis of financial management actions. The ELECTRE-III-H method generates a partial pre-order on a set of alternatives at different levels of a hierarchy of evaluation criteria. This method models the decision maker's preferences using three parameters on each of the intermediate criteria of the hierarchy. These parameters must take values depending on the positions of the alternatives in the partial pre-orders. For a non expert user, the definition of those values is not trivial. It requires a deep understanding of the underlying method in order to assign the appropriate values. In this work, some assisting tools have been designed and implemented to help the user to find the most suitable values for these parameters. First, some initial sets of parameters are automatically calculated by the system for some predefined common preference models. Second, the user has several tools to explore the space of possible values starting from these initial ones. The system also generates several graphics that display the results obtained using different parameters, so that the user can easily identify which ones represent his preferences.

关键词： Decision support systems Analysis tools preference modeling

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preference modeling experiments with surrogate weighting procedures for the PROMETHEE method

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第2期264卷 453-461页

作者： de Almeida Filho, Adiel T. Clemente, Tharcylla R. N. Morais, Danielle Costa de Almeida, Adiel Teixeira Univ Fed Pernambuco CDSID Av Acad Helio Ramos S-NCidade Univ BR-50740530 Recife PE Brazil

One of the main tasks in a multi-criteria decision-making process is to define weights for the evaluation criteria. However, in many situations, the decision-maker (DM) may not be confident about defining specific values for these weights and may prefer to use partial information to represent the values of such weights with surrogate weights. Although for the additive model, the use of surrogate weighting procedures has been already explored in the literature, there is a gap with regard to experimenting with such kind of preference modeling in outranking based methods, such as PROMETHEE, for which there already are applications with surrogate weights in the literature. Thus, this paper presents an experimental study on, preference modeling based on simulation so as to increase understanding and acceptance of a recommendation obtained when using surrogate weights within the PROMETHEE method. The main approaches to surrogate weights in the literature (EW, RS, RR and ROC) have been evaluated for choice and ranking problematics throughout statistical procedures, including Kendall's tau coefficient. The surrogate weighting procedure that most faithfully represents a DM's value system according to this analysis is the ROC procedure. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Multi criteria decision analysis preference modeling PROMETHEE method Partial information Surrogate weights

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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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Probabilistic fuzzy regression approach for preference modeling

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 64卷 286-294页

作者： Jiang, Huimin Kwong, C. K. Park, Woo-Yong Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China

Two types of uncertainty, namely, randomness and fuzziness, exist in preference modeling. Fuzziness is mainly caused by human subjective judgment and incomplete knowledge, and randomness often originates from the variability of influences on the inputs and outputs of a preference model. Various techniques have been utilized to develop preference models. However, only few previous studies have addressed both fuzziness and randomness in preference modeling. Among these limited studies, none have considered the randomness caused by particular independent variables. To fill this research gap, this study proposes probabilistic fuzzy regression (PFR), a new approach for preference modeling. PFR considers both the fuzziness of data sets and the randomness caused by independent variables. In the proposed approach, probability density functions (PDFs) are adopted to model randomness. The parameter settings of the PDFs are determined using a chaos optimization algorithm. The probabilistic terms of the PFR models are generated according to the expected value functions of the random variables. Fuzzy regression analysis is employed to determine the fuzzy coefficients for all the terms of the PFR models. An industrial case study of a tea maker design is used to illustrate the applicability of PFR and evaluate its effectiveness. modeling results obtained from PFR are compared with those obtained from statistical regression, fuzzy regression, and fuzzy least-squares regression. Results of the training and validation tests show that PFR outperforms the other approaches in terms of training and validation errors. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Probabilistic fuzzy regression preference modeling Chaos optimization algorithm

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Coherence conditions for preference modeling with ordered points

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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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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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