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Online social image ranking in diversified preferences

EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2020年第1期2020卷 1-28页

作者： Zhao, Xuezhuan Pei, Lishen Li, Tao Zhang, Zheng Zhengzhou Univ Aeronut Wenyuan Rd Zhengzhou 450046 Peoples R China Henan Univ Econ & Law Jinshui Rd Zhengzhou 450046 Peoples R China Civil Avit Univ China Informat Technol Res Base Civil Aviat Adm Tianjin 300300 Peoples R China HeNan Radio Televis Univ Wenyuan Rd Zhengzhou 450046 Peoples R China

Due to the prevalence of social media service, effective and efficient online image retrieval is in urgent need to satisfy diversified requirements of Web users. Previous studies are mainly focusing on bridging the semantic gap by well-established content modeling with semantic information and social tagging information, but they are not flexible in aggregating the diversified expectations of the online users. In this paper, we present OSIR, a solution framework to facilitate the diversified preference styles in online social media image searching by textual query inputs. First, we propose an efficient Online Multiple Kernel Ranking (OMKR) model which is constructed on multiple query dimensions and complimentary feature channels, and trained by minimizing the triplet loss on hard negative samples. By optimizing the ranking performance with multi-dimensional queries, the semantic consistency between the image ranking and textual query input is directly maximized without relying on the intermediate semantic annotation procedure. Second, we construct random walk-based preference modeling by domain-specific similarity calculation on heterogeneous social attributes. By re-ranking the rank output of OMKR based on each preference ranking model, we obtain a set of ranking lists encoding different potential aspects of user preference. Last, we propose an effective and efficient position-sensitive rank aggregation approach to aggregate multiple ranking results based on the user preference specification. Extensive experiment on two social media datasets demonstrates the advantages of our approach in both retrieval performance and user experience.

关键词： Social media image retrieval Online multiple kernel Ranking preference modeling Rank aggregation

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A multiple criteria approach integrating social ties to support purchase decision

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 147卷 106655-106655页

作者： Liang, Qian Liao, Xiuwu Shang, Jennifer Xi An Jiao Tong Univ Sch Management Key Lab Minist Educ Proc Control & Efficiency Engn Xian 710049 Shaanxi Peoples R China Univ Pittsburgh Joseph M Katz Grad Sch Business Pittsburgh PA 15260 USA

To leverage information sharing in online social networks, online retailers (e-tailers) have launched social networking functions on their platforms. The objective of e-tailers' social networks is to empower consumers to connect and share product information. However, the e-tailers have not used these social networks to provide product recommendations to customers. Our goal is to aid e-retailers in personalizing recommendations for customers using their friends' (referrers') preferences. Our approach involves: (a) mapping the referrers' online behaviors into a set of pairwise comparisons, and use additive value functions to model their preferences, (b) defining the degree of contextual trust of a customer towards a referrer to differentiate the roles of referrers, (c) proposing a clustering algorithm to capture referrers' heterogeneous preferences, and (d) aggregating preference information for referrers within the same subgroup to obtain diversified recommendations. On a broader note, this study illustrates how online information (i.e., preference expressions of referrers, social ties between a consumer and referrers) from an e-tailer's social network can be mined and incorporated into a decision-aiding approach to generate tailor-made recommendations for customers. Finally, we illustrate the proposed approach to with a numerical case study.

关键词： E-tailers' social networks Social ties preference modeling Multiple criteria analysis

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Market segmentation: A multiple criteria approach combining preference analysis and segmentation decision

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2019年 83卷 1-13页

作者： Liu, Jiapeng Liao, Xiuwu Huang, Wei Liao, Xianzhao Xi An Jiao Tong Univ Sch Management Minist Educ Proc Control & Efficiency Engn Key Lab Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Shaanxi Peoples R China

We propose a new multiple criteria decision aiding approach for market segmentation that integrates preference analysis and segmentation decision within a unified framework. The approach employs an additive value function as the preference model and requires consumers to provide pairwise comparisons of some products as the preference information. To analyze each consumer's preferences, the approach applies the disaggregation paradigm and the stochastic multicriteria acceptability analysis to derive a set of value functions according to the preference information provided by each consumer. Then, each consumer's preferences can be represented by the distribution of possible rankings of products and associated support degrees by applying the derived value functions. On the basis of preference analysis, a new metric is proposed to measure the similarity between preferences of different consumers, and a hierarchical clustering algorithm is developed to perform market segmentation. To help firms serve consumers from different segments with targeted marketing policies and appropriate products, the approach proposes to work out a representative value function and the univocal ranking of products for each consumer so that products that rank in the front of the list can be presented to her/him. Finally, an illustrative example of a market segmentation problem details the application of the proposed approach. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Multiple criteria decision aiding Market segmentation preference modeling Clustering analysis

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Multiple criteria hierarchy process for sorting problems under uncertainty applied to the evaluation of the operational maturity of research institutions

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2021年 103卷 102381-102381页

作者： Pelissari, Renata Abackerli, Alvaro Jose Ben Amor, Sarah Oliveira, Maria Celia Infante, Kleber Manoel Univ Ottawa Telfer Management Sch Ottawa ON Canada Univ Metodista Piracicaba Post Grad Program Ind Engn Santa Barbara Doeste SP Brazil Univ Estadual Campinas Sch Appl Sci Limeira Brazil Embrapii Brazilian Assoc Ind Res & Innovat Brasilia DF Brazil Univ Prebiteriana Mackenzie Sch Engn Sao Paulo Brazil KMI Software Consulting Jundiai Brazil

Despite the availability of highly qualified research personnel, up-to-date research facilities and experience in developing applied research and innovation, many worldwide research institutions face challenges when managing contracted Research and Development (R&D) projects. These difficulties are mainly due to expectations from Industry (private sector), particularly regarding the applied development procedures, managerial processes and timing. Such difficulties have motivated funding agents to create evaluation processes to check whether the operational procedures of funded research institutions are sufficient to provide timely answers to demand innovation from industry, and also to identify aspects that require quality improvement in research development. For this purpose, several multiple criteria decision-making approaches can be applied. In this context, the research institutions are considered as alternatives for funding and their processes for research development as decision criteria. Among the available multiple criteria approaches, sorting methods are one prominent tool to evaluate operational capacity. However, the first difficulty that one may face when applying multiple criteria sorting methods is the need to hierarchically structure multiple criteria in order to represent the intended decision process. Additional challenges include the elicitation of the preference information and the definition of criteria evaluation, since these are frequently affected by some imprecision. In most approaches, all these critical points are neglected, or, at best, only partially considered. In this paper, a new sorting method is proposed to deal with all of those critical points simultaneously. To consider multiple levels for the decision criteria, the FlowSort method is extended to account for hierarchical criteria. To deal with imprecise data, FlowSort is integrated with fuzzy approaches. To yield solutions that consider fluctuations from imprecise weights, the Sto

关键词： preference modeling Stochastic Multicriteria Acceptability Analysis Hierarchy criteria SMAA-FFS Operational maturity evaluation Research funding

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Consumer preference analysis: A data-driven multiple criteria approach integrating online information

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2020年 96卷 102074-102074页

作者： Guo, Mengzhuo Liao, Xiuwu Liu, Jiapeng Zhang, Qingpeng Xi An Jiao Tong Univ Sch Management Key Lab Minist Educ Proc Control & Efficiency Engn Xian 710049 Shaanxi Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

Multiple criteria approaches can assist the product manager to know the consumer preferences in the context of e-commerce. Consumer preference analysis explains what aspects of a product affect and how they affect a consumer's purchasing decision. This issue plays an important role in e-commerce platforms from its relevance in marketing decisions such as advertisements, recommendations and promotions. In this regard, we propose a data-driven multiple criteria decision aiding (MCDA) approach to integrate online information, such as explicit (e.g., reviews and ratings) and implicit (e.g., clicks and purchases) feedback from consumers. However, MCDA approaches present a critical challenge that even an experienced product manager could find it difficult to pre-define the criteria on which a product is evaluated. To address this issue, our proposed approach first utilizes text-mining techniques to assist the product manager identify the criteria, and then determines and collects the relative importance of the criteria and their values. Given the criteria information, we use a sampling process to provide two indices, the consumer preference index and rank acceptability index. The first index helps in prioritizing the pairwise comparisons of products, while the second one helps in deriving a default ranking list for first-time-registered consumers. We record the products viewed by consumers and generate their preference information in the form of pairwise comparisons for analyses within an aggregation-disaggregation paradigm. We also provide a representative value function to help the product manager gain insight into the preferences. Finally, we describe how a real-world application including the product manager and consumers exploits the proposed approach on an e-commerce platform to take a large step toward aiding more realistic and data-driven multiple criteria decision making. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Multiple criteria decision making preference analysis preference modeling E-commerce Online reviews

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Increasing provision of ecosystem services through participation in a conservation program

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ECOSYSTEM SERVICES 2021年 50卷 101303-101303页

作者： Jayalath, Tharaka A. Grala, Robert K. Grado, Stephen C. Evans, David L. Mississippi State Univ Dept Forestry Box 9681 Mississippi State MS 39762 USA

Grasslands provide a variety of goods and services that benefit humans, but only a few have a market value. Despite the benefits provided, native grasslands continue to be degraded. Lack of a proper valuation, including a monetary value of grassland nonmarket goods and services, has become one of the factors contributing to the degradation trend. The objective of this research was to quantify the cost of increasing the provision of ecosystem services from grassland ecosystems in the Gulf Coastal Plains and Ozarks (GCPO) Landscape Conservation Cooperative geographic area of the U.S. A contingent valuation method (CVM) was used to estimate landowner willingness to accept (WTA) monetary compensation in exchange for implementing management practices preserving grassland ecosystems. The mean WTA compensation was $290.10 per acre per year for a 10-year contract. Required compensation increased with landowner income, frequency of contacts with state wildlife agencies, and percentage change in grassland area. Landowner age and proximity to the nearest local park were negatively associated with WTA. The total cost of preserving ecosystem services, estimated based on WTA, was US$3 billion per year and 57 times larger than the 2016 Conservation Reserve Program (CRP) allocation for this purpose in the amount US$50 million. However, the preservation cost varied from US$1.7 million to 1.1 billion depending upon the number of ecological attributes to be preserved and corresponding levels of ecosystem services. Results will be helpful in quantifying future funding levels necessary for implementation of coordinated conservation activities in grasslands and other ecosystems, prioritizing ecosystem services for restoration, and developing conservation programs in the U.S. and other countries.

关键词： Contingent valuation method Monetary compensation preference modeling Willingness to accept Voluntary conservation

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Improving Algorithm Response to preference Changes in Multiobjective Optimisation Using Archives

Improving Algorithm Response to Preference Changes in Multio...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Taylor, Kendall Li, Xiaodong Chan, Jeffrey RMIT Univ Sch Sci Melbourne Vic Australia

ISBN: (纸本)9781728121536

Using evolutionary algorithms to solve optimisation problems with multiple objectives has proven very successful over the past few decades. The ability of these methods to efficiently find sets of solutions representing trade-offs between conflicting objectives has enhanced decision making in a wide variety of fields. Increasingly though, such techniques are being adapted to incorporate end-user preferences in order to reduce search spaces and provide smaller sets of targeted solutions. Eliciting these preferences interactively during optimisation has also become popular and helps a decision maker explore and learn and the problem and its range of solutions. Interactivity also facilitates the correction of mistakes and inaccurate preferences, leading to more satisfactory solutions, faster. In order to achieve these benefits an algorithm must be able to rapidly respond to changes in preferences. This work explores the use of secondary population archives to ensure a preference-based algorithm can change its search focus efficiently and effectively. When preferences change and the search is redirected to a new region of interest, an archive of previously found solutions can be consulted and solutions close to the new region can be included in the current population. This work shows how such archives can be implemented and how they can improve responsiveness for certain problems.

关键词： Interactive optimisation preference modeling Multiobjective optimisation Evolutionary computation

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Heterogeneous Interrelationships among Attributes in Multi-Attribute Decision-Making: An Empirical Analysis

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第2期12卷 984-997页

作者： Chen, Zhen-Song Zhang, Xuan Rodriguez, Rosa M. Wang, Xian-Jia Chin, Kwai-Sang Wuhan Univ Sch Civil Engn Wuhan Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China Univ Jaen Dept Comp Sci Jaen Spain Wuhan Univ Econ & Management Sch Wuhan Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China

Tremendous effort has been exerted over the past few decades to construct multi-attribute decision functions with the capacity to model heterogeneous interrelationships among attributes. In this paper, we report an empirical study aiming to test whether or not considering interrelationships among attributes can benefit the representation of real preferences in multi-attribute ranking tasks. The generalized extended Bonferroni mean (GEBM) has recently been advocated as a promising and efficient tool for modeling heterogeneous interrelationships among attributes. We compare the GEBM with one of its most widely adopted competitors, simple additive weighting (SAW), in terms of their fitting quality when applied to preference elicitation. The attribute performances are manifested uniformly with the use of three widely-adopted utility measurements. Subsequently afterwards, the maximum split approach to establish the constraint objective function in regression for both the GEBM and the SAW to test whether or not all constraints resulting from the subject's ranking can be fulfilled. On this bases, the number of fully or partly fitted subjects, consistency for subjects according to the better fitting model, and reliability of attribute weights learned by either the GEBM or the SAW are empirically examined in a bid to demonstrate the quantitative construction of fitting quality measurement. With the established fitting quality measurement, the necessity of taking heterogeneous interrelationships among attributes into account when constructing multi-attribute decision functions to represent real preferences can be analyzed. The main conclusion from the empirical study suggests that the relative performance of the two aggregation paradigms examined here depends on which fitting quality measurements are adopted. Researchers enthusiastic to discover the heterogeneous interrelationships among attributes when constructing multi-attribute decision functions might find the present re

关键词： Heterogeneous interrelationships Generalized extended Bonferroni mean Simple additive weighting preference modeling Multi-attribute decision function

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Identifying non-additive multi-attribute value functions based on uncertain indifference statements

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2019年 85卷 49-67页

作者： Haag, Fridolin Lienert, Judit Schuwirth, Nele Reichert, Peter Eawag Swiss Fed Inst Aquat Sci & Technol Uberlandstr 133 CH-8600 Dubendorf Switzerland Swiss Fed Inst Technol Inst Biogeochem & Pollutant Dynam Univ Str 16 CH-8092 Zurich Switzerland

Multi-criteria decision analysis (MCDA) requires an accurate representation of the preferences of decision makers, for instance in the form of a multi-attribute value function. Typically, additivity or other stringent assumptions about the preferences are made to facilitate elicitation by assuming a simple parametric form. When relaxing such assumptions, parameters cannot be elicited easily with standard methods. We present a novel approach for identifying multi-attribute value functions which can have any shape. As preference information indifference statements are used that can be elicited by trade-off questions. Instead of asking one indifference statement for each pair of attributes, we ask for multiple trade-offs at different points in the attribute space. This allows inferring parameters of complex value functions despite the simplicity of the preference statements. Parameters are estimated by taking into account preference and elicitation uncertainty with a probabilistic model. Statistical inference supports identifying the most adequate preference model out of several candidate models through quantifying the uncertainty and assessing the need for non-additivity. The approach is elaborated for determining value functions by hierarchical aggregation. We apply it to an assessment of the ecological state of rivers, which is used to support environmental management decisions in Switzerland. preference models of four experts were quantified, confirming the feasibility of the approach and the relevance of considering non-additive functions. The method suggests a promising direction for improving the representation of preferences. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： decision making/process preference modeling multi-attribute value theory uncertainty aggregation environmental assessment

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How to implement MCDM tools and continuous logic into neural computation?

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KNOWLEDGE-BASED SYSTEMS 2020年 210卷 106530-106530页

作者： Csiszar, Orsolya Csiszar, Gabor Dombi, Jozsef Obuda Univ Inst Appl Math Budapest Hungary Univ Appl Sci Esslingen Fac Basic Sci Esslingen Germany Univ Stuttgart Inst Mat Sci Dept Mat Phys Stuttgart Germany Univ Szeged Inst Informat Szeged Hungary

The theories of multi-criteria decision-making (MCDM) and fuzzy logic both aim to model human thinking. In MCDM, aggregation processes and preference modeling play the central role. This paper suggests a consistent framework for modeling human thinking by using the tools of both fields: fuzzy logical operators as well as aggregation and preference operators. In this framework, aggregation, preference, and the logical operators are described by the same unary generator function. Similarly to the implication being defined as a composition of the disjunction and the negation operator, preference operators were introduced as a composition of the aggregative operator and the negation operator. After a profound examination of the main properties of the preference operator, our main goal is the implementation into neural networks. We show how preference can be modeled by a perceptron, and illustrate the results in practical neural applications. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Multi-criteria decision-making preference modeling Outranking methods Fuzzy systems Continuous logic Nilpotent logical systems Neural networks

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