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Comprehensive Rules-Based and preferences Induced Weights Allocation in Group Decision-Making with BUI

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-8页

作者： Li, GePeng Yager, Ronald R. Zhang, XinXing Mesiar, Radko Bustince, Humberto Jin, LeSheng Nanjing Normal Univ Sch Business Nanjing Peoples R China King Abdulaziz Univ Fac Sci Jeddah Saudi Arabia Iona Coll Inst Machine Intelligence New Rochelle NY 10801 USA Slovak Univ Technol Bratislava Fac Civil Engn Radlinskeho 11 SK-81005 Bratislava Slovakia Palacky Univ Dept Algebra & Geometry Fac Sci 17 Listopadu 12 Olomouc 77146 Czech Republic Univ Publ Navarra Dept Stat Comp Sci & Math Campus Arrosadia Sn Pamplona 31006 Spain

Decision-makers' subjective preferences can be well modeled using preference aggregation operators and related induced weights allocation mechanisms. However, when several different types of preferences occur in some decision environment with more complex uncertainties, repeated uses of preferences induced weights allocation sometimes become unsuitable or less reasonable. In this work, we discuss a common decision environment where several invited experts will offer their respective evaluation values for a certain object. There are three types of preferences which will significantly affect the weights allocations from experts. Instead of unsuitably performing preference induced weights allocation three times independently and then merging the results together using convex combination as some literatures recently did, in this work, we propose some organic and comprehensive rules-based screen method to first rule out some unqualified experts and then take preference induced weights allocation for the refined group of experts. A numerical example in business management and decision-making is presented to show the cognitive reasonability and practical feasibility.

关键词： Aggregation operators Basic uncertain information Decision-making Information fusion preference modeling

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preference modeling for Personalized Retrieval Based on Browsing History Analysis

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2013年第Sup1期8卷 S81-S87页

作者： Zhang, Xiaoyu Inst Sci & Tech Informat China Ctr Sci & Technol Studies Beijing 100038 Peoples R China

Personalized retrieval aims at meeting the personalized information need of users, in which preference modeling is of great importance. The user's preference can be revealed via user specification and relevance feedback, both of which require extra effort from the user and are inevitably labor intensive. In this paper, we propose a novel preference modeling algorithm based on browsing history analysis for personalized retrieval. Based on the browsing log, we explore the user's interest in both fields and field values and accumulatively update the preference model. Given a query, the relevant retrieval results can spontaneously be ranked according to their corresponding preference score without any extra user interference. Advanced settings are subsequently discussed to further improve the algorithm for practical use. Experimental results demonstrate the advantages of the proposed algorithm over previous work. (c) 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： personalized information retrieval preference modeling browsing history analysis user experience relevance feedback

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Neuroscience Behavioral Studies for Modulation of the FITradeoff Method 22nd

Neuroscience Behavioral Studies for Modulation of the FITrad...

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22nd International Conference on Group Decision and Negotiation (GDN)

作者： Peixoto Roselli, Lucia Reis de Almeida, Adiel Teixeira Univ Fed Pernambuco CDSID Recife PE Brazil

ISBN: (纸本)9783031079955;9783031079962

It has been claimed in the literature that decision-making methods have not been modulated (transformed) by results obtained in behavioral studies as much as has been expected and that further modulation would be an important advancement in decision-making. This paper summarizes the modulation provided by the Flexible and Interactive Tradeoff (FITradeoff) method from behavioral studies performed using neuroscience tools. Modulations of the FITradeoff method have been conducted in twoways: modulations in the preference modelling process and modulations in the FITradeoff Decision Support System (DSS). For modulation in FITradeoff preference modeling, several recommendations were provided to support analysts during their advising process with decision-makers. For modulation in the FITradeoff DSS, several improvements were implemented in the design of the DSS. The modulation of the FITradeoff method was supported by neuroscience experiments. These experiments investigated decision-makers' (DMs) behavior when they interacted with a holistic evaluation and elicitation by decomposition in the FITradeoff method. The modulation of the FITradeoff method promoted the inclusion of some features through the combination of the two paradigms of preference modeling, completely transforming the decision-making process, and its DSS.

关键词： Modulation FITradeoff method preference modeling Decision support system Behavioral studies

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Conditional preference Networks Refining Solution Orderings Beyond Pareto Dominance 34th

Conditional Preference Networks Refining Solution Orderings ...

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34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Ben Amor, Nahla Dubois, Didier Prade, Henri Saidi, Syrine ISG Tunis LARODEC Lab 41 Rue Liberte Le Bardo 2000 Tunisia IRIT CNRS 118 Route Narbonne F-31062 Toulouse 09 France

ISBN: (纸本)9783030794569;9783030794576

When handling preferences, the inclusion-based partial order (called here Pareto dominance) is a natural basis for comparing solutions: one is then preferred to another as soon as the set of preferences violated by the former is a subset of the preferences violated by the latter. In order to obtain refinements of this partial order, various general principles can be used, such as Ceteris Paribus, or optimistic (resp. pessimistic) principles stating that what is not explicitly said to be satisfactory according to the preferences should be regarded as satisfactory (resp. disregarded). Algorithms taking advantage of the two last principles are presented and discussed. They enable us to refine the Pareto ordering of solutions.

关键词： preference modeling Possibilistic network Default reasoning Pareto ordering

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Revisiting Fuzzy and Linguistic Decision Making: Scenarios and Challenges for Making Wiser Decisions in a Better Way

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第1期51卷 191-208页

作者： Herrera-Viedma, Enrique Palomares, Ivan Li, Cong-Cong Cabrerizo, Francisco Javier Dong, Yucheng Chiclana, Francisco Herrera, Francisco Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada 18071 Spain Southwest Jiaotong Univ Sch Econ & Management Chengdu 610031 Peoples R China Sichuan Univ Ctr Network Big Data & Decis Making Business Sch Chengdu 610065 Peoples R China De Montfort Univ Sch Comp Sci & Informat Inst Artificial Intelligence Leicester LE1 9BH Leics England

This article provides a brief tour through the main fuzzy and linguistic decision-making trends, studies, methodologies, and models developed in the last 50 years. Fuzzy and linguistic decision-making approaches allow to address complex real-world decision problems where humans exhibit vagueness, imprecision, and/or use natural language to assess decision alternatives, criteria, etc. The aim of this article is threefold. First, the main fuzzy set theory and computing with words-based representation paradigms of decision information, with their different levels of expressive richness and complexity, are reviewed. Second, three core decision-making frameworks are examined: 1) multicriteria decision making;2) group consensus-driven decision making;and 3) multiperson multicriteria decision making. Third, the article discusses new complex decision-making frameworks that have emerged in recent years, where decisions are guided by the "wisdom of the crowd": their associated challenges are highlighted and considerations on much needed key guidelines for future research in the field are provided.

关键词： Consensus reaching processes (CRPs) fuzzy decision making fuzzy sets group decision making (GDM) linguistic decision making multicriteria decision making (MCDM) preference modeling

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Enabling Autonomous Decision-Making in Manufacturing Systems through preference Fusion

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SAE INTERNATIONAL JOURNAL OF MATERIALS AND MANUFACTURING 2020年第2期13卷 109-124页

作者： Slon, Christopher Pandey, Vijitashwa Oakland Univ Rochester MI 48309 USA

Decision analysis (DA), a well-established discipline in business and engineering, is entering another domain of application due to the advent of Industry 4.0. DA enables optimal decisions by finding system parameters that maximize the utility, or in the presence of uncertainty the expected utility, from the attributes of a system. Whether there is a single decision maker or all decision makers have uniform preferences, determining risk behavior and the resulting utility is well developed in the existing literature. However, variability in preferences has not been satisfactorily addressed. This gap gains added significance in the face of the demands of Industry 4.0 where cyberphysical production systems must drive autonomous decision-making on the factory floor. The decisions must accommodate a distribution of customer and designer preferences, including production auditors within the organization. This article provides a novel framework and develops a closed-form approximation for expected utility in the presence of uncertainty in both attributes and preference behaviors. The value of this approach is demonstrated in the assembly of parts in a cyberphysical production system of an automotive manufacturing plant. The comparison of corrective assembly using the proposed method with existing random assembly approaches shows significant improvements.

关键词： Manufacturing Systems Industry 4.0 Decision Analysis preference modeling Assembly

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Prioritizing Improvement Actions in a Fish Distribution Company: Integrating Elicitation by Decomposition and Holistic Evaluation with FITradeoff Method 21st

Prioritizing Improvement Actions in a Fish Distribution Comp...

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21st International Conference on Group Decision and Negotiation (GDN)

作者： Correia, Marina Carvalhedo Frej, Eduarda Asfora de Almeida, Adiel Teixeira Univ Fed Pernambuco UFPE CDSID Ave Acad Helio Ramos S-NCidade Univ BR-50740530 Recife PE Brazil

ISBN: (纸本)9783030772079;9783030772086

In this paper, a prioritization problem of strategic improvement actions of a Fresh Fish DistributionCompany (FFDC) is approached. Improvement actions to be prioritized are defined based on critical success factors derived from the SWOT analysis of the company, conducted by multiple stakeholders of the organization. Four criteriawere defined to evaluate these actions, and then the FITradeoff (Flexible and Interactive Tradeoff) multicriteria method is applied to aid the decision process and preferences assessment. This method works based on partial information of the DMs to elicit criteria scaling constants in additive models, considering structured elicitation process based on tradeoffs. Throughout an interactive Decision Support System (DSS), this work illustrates how the decision process can be carried out with FITradeoff based on an integration of two paradigms in preference modeling: elicitation by decomposition and holistic evaluations.

关键词： Strategic planning Multicriteria decision making/aiding (MCDM/A) preference modeling FITradeoff method

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Comparing The Accuracy of Frequentist and Bayesian Models in Human-Agent Negotiation 21

Comparing The Accuracy of Frequentist and Bayesian Models in...

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21st ACM International Conference on Intelligent Virtual Agents (IVA)

作者： Johnson, Emmanuel Gratch, Jonathan Univ Southern Calif Playa Vista CA 90094 USA

ISBN: (纸本)9781450386197

Understanding an opponent's wants is crucial for maximizing the outcomes of a multi-issue negotiation. To do this, automated systems must build an "opponent model" from information conveyed during a negotiation. Bayesian and frequentist models are the most commonly used. Bayesian models have a principled way to incorporate prior knowledge about an opponent's preferences. However, frequentist models have outperformed Bayesian approaches in practice, dominating the yearly agent-verses-agent negotiation competitions. With growing interest in agents that negotiate with people, this presumed dominance needs to be revisited. Human opponents convey far less information than automated agents, and people often share similar preferences (e.g., in a salary negotiation, most people care the most about salary). Thus, the theoretical advantage of Bayesian approaches may translate into practice for agent-versus-human negotiation. In this work, we compare the performance of Bayesian models against a leading frequentist approach in an agent-versus-human multi-issue salary negotiation. Although we show that frequentist opponent models outperform Bayesian models when using a uniform prior, Bayesian approaches excel when using two common priors. The best performance is achieved with an empirically-derived prior (i.e., biasing the model space using the distribution of preferences found in past human negotiators). Yet, strong performance is also observed when using a "fixed-pie bias", the prior used by most human negotiators. We discuss the implication of these findings for research on human-agent negotiation.

关键词： Opponent modeling preference modeling negotiation

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Joint modeling Dynamic preferences of Users and Items Using Reviews for Sequential Recommendation 25th

Joint Modeling Dynamic Preferences of Users and Items Using ...

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25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Shang, Tianqi Li, Xinxin Shi, Xiaoyu Wang, Qingxian Sichuan Univ Coll Comp Sci Chengdu 610207 Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China

ISBN: (纸本)9783030757656;9783030757649

The emerging of sequential recommender (SR) has attracted increasing attention in recent years, which focuses on understanding and modeling the temporal dynamic of user behaviors hidden in the sequence of user-item interactions. However, with the tremendous increase of users and items, SR still faces several challenges: (1) the hardness of modeling user interests from spare explicit feedback;(2) the time and semantic irregularities hidden in the user's successive actions. In this study, we present a neural network-based sequential recommender model to learn the temporal-aware user preferences and item popularity jointly from reviews. The proposed model consists of the semantic extracting layer and the dynamic feature learning layer, besides the embedding layer and the output layer. To alleviate the data sparse issue, the semantic extracting layer focuses on exploiting the enriched semantic information hidden in reviews. To address the time and semantic irregularities hidden in user behaviors, the dynamic feature learning layer leverages convolutional fitters with varying size, integrating with a time-ware controller to capture the temporal dynamic of user and item features from multiple temporal dimensions. The experimental results demonstrate that our proposed model outperforms several state-of-art methods consistently.

关键词： Sequential recommendation preference modeling Temporal dynamic Semantic extracting Deep learning

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ART (Attractive Recommendation Tailor): How the Diversity of Product Recommendations Affects Customer Purchase preference in Fashion Industry? 20

ART (Attractive Recommendation Tailor): How the Diversity of...

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29th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Kwon, Hyokmin Han, Jaeho Han, Kyungsik LF Corp Seoul South Korea Ajou Univ Dept Comp Engn Suwon South Korea

ISBN: (纸本)9781450368599

This study examines the impact of the "diversity" of product recommendations on the "preference" of a customer, using online/offline data from a leading fashion company. First, through interviews with fashion professionals, we categorized the characteristics of customers into four types - gift, coordinator, carry-over, and trendsetter. Then, using a hybrid filtering method, we increased the accuracy and diversity of recommended products. We derived 13 salient features that reflect customer behavior based on the Purchase Funnel model and built a classification model that predicts a customer's preference rates. Second, we conducted two large-scale user tests with 20,000 real customers to verify the effectiveness of our recommendation system. Study results empirically demonstrated the importance of diversity of recommended products. The more diverse the product recommendations were, the higher the purchase rate, the average purchase amount, and the cross purchase rate were observed. In addition, we tracked the customers' purchase for two months after the user tests and found that diverse product exposure positively influenced customer retention (e.g., repurchase rate, amount).

关键词： Fashion recommendation Diversity Feature engineering preference modeling Large-scale user test

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