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preference prediction analysis based on graph model for environmental governance conflict

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第6期19卷 6141-6156页

作者： Zhao, Jinshuai Yang, Baohua Jiangsu Normal Univ Sch Comp Sci & Technol 101 Shanghai Rd Xuzhou 221116 Jiangsu Peoples R China Jiangsu Normal Univ Sch Business 101 Shanghai Rd Xuzhou 221116 Jiangsu Peoples R China

A mathematical model for decision maker's preference prediction in environmental governance conflict is established based on the graph model for conflict resolution. The rapid economic development in many countries, over the past decades, has caused serious environmental pollution. Sewage companies are the main source of contamination since they are always wavering on the issue of environmental governance because of their profit-seeking nature. Environmental management departments cannot grasp the offending company preferences accurately. The problem of how to obtain decision maker's preference in environmental governance conflict is studied in this paper. The mathematical model established in this paper can obtain a preference set of one decision maker on the promise that the ideal conflict outcome and preference of the other decision makers are known. Then, preference value distribution information entropy is introduced to mine the preference information contained in the preference set, which means that it is possible to get the preference information of conflict opponent at their own ideal conflict outcome. All of these preference sets provide the possibility to choose the appropriate coping strategies and lead the conflict to the direction that some decision makers want. Finally, the effectiveness and superiority of the preference prediction analysis method is verified through a case study of "Chromium Pollution in Qujing County" which took place in China. The preference prediction analysis method in this paper can provide decision making support for the decision makers in environmental governance from strategic level.

关键词： environmental governance preference prediction environmental protection decision making graph model conflict analysis environmental pollution

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Hierarchical Graph Neural Network for Patient Treatment preference prediction with External Knowledge 27th

Hierarchical Graph Neural Network for Patient Treatment Pref...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Li, Quan Chen, Lingwei Cai, Yong Wu, Dinghao Penn State Univ University Pk PA USA Wright State Univ Dayton OH USA Calif State Univ Monterey CA 93955 USA IQVIA Inc Wayne PA 19087 USA

ISBN: (纸本)9783031333798;9783031333804

The healthcare industry has a wealth of data that can be used by researchers and medical professionals to infer a patient's condition and intention to receive treatment using machine learning models. However, this line of research generally suffers from some limitations: (1) struggling to leverage structural interactions among patients;(2) attending to learn patient representations from electronic medical records (EMRs) but rarely considering supplementary contexts;and (3) overlooking EMR data imbalance issue. To address these limitations, in this paper, we propose a hierarchical graph neural network for patient treatment preference prediction. Doctors' information and their viewing activities are first integrated as external knowledge with EMRs to construct the hierarchical graph, where a dual message passing paradigm is then devised to perform intra- and inter-subgraph aggregation to enrich patient representations and advance label propagation. To mitigate patient data imbalance issue, a community detection method is further designed to better prediction. Our experimental results demonstrate the state-of-the-art performance on patient treatment preference prediction.

关键词： Hierarchical graph neural network Oncology treatments preference prediction Healthcare Community detection

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Application of E-Commerce Recommendation Algorithm in Consumer preference prediction

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JOURNAL OF CASES ON INFORMATION TECHNOLOGY 2022年第5期24卷 1-28页

作者： Wang, Wei Anhui Business & Technol Coll Sch Econ & Trade Wuhu Peoples R China

Through user characteristic information, user interaction behavior, commodity characteristic information, recommendation engine, and related technologies in data mining, this paper makes a more in-depth study and analyzes the problems of "big data volume," "cold start," and "data sparsity" in the recommender system in modern business websites. In response to these problems, this paper transforms the problem of large data volume into the problem of large user groups. Then, after using the k-means clustering algorithm to divide the large user group into homogeneous user groups to alleviate the problem, a combination of collaborative filtering algorithm and content-based recommendation algorithm in the homogeneous user group is proposed to alleviate this problem. The experimental precision and recall are both around 0.4, and when W=0.8, the F value is the largest.

关键词： Algorithm Collaborative Filtering Recommendation Electronic Commerce preference prediction Recommendation

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Data-driven implicit design preference prediction model for product concept evaluation via BP neural network and EEG

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ADVANCED ENGINEERING INFORMATICS 2023年 58卷

作者： Jing, Liting Tian, Chulin He, Shun Feng, Di Jiang, Shaofei Lu, Chunfu Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China Zhejiang Univ Technol Ind Design Inst Hangzhou 310023 Peoples R China

Customer preference-involved product concept evaluation is a vital stage for developing new products. One of the key factors in this stage is to mine the potential relationships between customers' needs and technical requirements. In most new product development (NPD), design characteristics (DCs) are employed as performance indicators to describe the technical requirements of a scheme. Previous studies have focused on evaluating the degree to which a scheme meets the performance value of DCs, disregarding the relationship between DCs and potential user preferences. This oversight leads to an inability to accurately predict customer satisfaction with the candidate schemes based on their preferences. Previous research, which was aimed at ensuring the preference value of schemes by utilizing mapping data between design preference (DP) and DCs, overlooked users' psychological states and thus failed to accurately capture their cognitive preferences for the schemes. To address these issues, an electroencephalogram (EEG) data-driven, implicit DP prediction approach is proposed. The method characterizes the implicit cognitive relationship between DP and DCs by mining EEG data and utilizes a back propagation (BP) neural network to predict DP. First, a user's DP word pairs were constructed based on the approach of statistical analysis, and the best-worst method (BWM) was introduced to obtain the importance of DCs. Second, an experiment was designed based on the extracted preference word pairs to collect EEG data that reflect a user's DC preference, record the user's stimulation state toward the DCs, and label their DP based on the segmentation results of preference evaluation. Third, a BP neural network was employed to construct a preference prediction model for DCs, and the reliability of the proposed preference prediction model was validated by using the forklift design example. The prediction results further exhibited the potential association between user preferences and

关键词： Implicit design preference Design characteristics EEG data Neural network model preference prediction

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A Novel User preference prediction Model Based on Local User Interaction Network Topology 6th

A Novel User Preference Prediction Model Based on Local User...

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6th International Conference on Communications, Signal Processing, and Systems (ICCSPS)

作者： You, Siqing Zhou, Li Liu, Yan Liu, Hongjie Xue, Fei Beijing Wuzi Univ Sch Informat Beijing 101149 Peoples R China Beijing Adv Innovat Ctr Future Internet Technol Beijing 100124 Peoples R China Beijing Wuzi Univ Sch Logist Beijing 101149 Peoples R China

ISBN: (纸本)9789811065712;9789811065705

As people's decisions are influenced by their social relationships, social networks have been widely applied in user behavior analysis, preference prediction and recommendation. However, static social relationship in a network alone is insufficient to model interpersonal influence and predict user preferences. In this paper, we propose a local user interaction network topology (LUINT) model to calculate the social influence between neighbors, which takes into account three types of user interactions: "at" action, comment, and re-tweet. Moreover, we design and adopt a shortest path with maximum propagation (SPWMP) algorithm to model the influence propagation within the network. To evaluate our approach, experiments on data set KDD Cup 2012, Track 1 are conducted. The results indicate that the proposed model significantly outperforms the other benchmark methods in predicting preference of the users.

关键词： User interaction Social influence and propagation User behavior analysis preference prediction Social network

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Service Recommendation Based on User Dynamic preference Extraction and prediction

Service Recommendation Based on User Dynamic Preference Extr...

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IEEE World Congress on Services (IEEE SERVICES)

作者： Zhang, Yanmei Qian, Ya Gan, Mengjiao Tang, Xiaoyi Lin, Zheng Cent Univ Finance & Econ Informat Sch Beijing 100081 Peoples R China

ISBN: (纸本)9781728138510

User preferences are dynamically changing due to a variety of factors. Therefore, how to effectively extract the user's current preferences and accurately predict subsequent user preferences is one of the keys to improving service recommendation. This paper proposes a deep learning-based user preference prediction method, which combines user dynamic preference extraction and preference prediction. Firstly, the time-series LDA model is used to extract the user dynamic preference, then the LSTM neural network method is used to predict the user's preference of next time slice based on the user dynamic preference sequence. Finally, services are recommended to users based on predicted preference. The experimental results on real-world dataset show that the prediction performance of Time-aware-gate LSTM is slightly better than traditional LSTM, and the recommendation performance of the recommendation algorithm that takes preference prediction into account is better than those without preference prediction.

关键词： service recommendation collaborative filtering probabilistic topic model preference prediction long short term memory neural network

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Bayesian-based preference prediction in bilateral multi-issue negotiation between intelligent agents

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KNOWLEDGE-BASED SYSTEMS 2015年第Aug.期84卷 108-120页

作者： Zhang, Jihang Ren, Fenghui Zhang, Minjie Univ Wollongong Sch Comp Sci & Software Engn Wollongong NSW Australia

Agent negotiation is a form of decision making where two or more agents jointly search for a mutually agreed solution to a certain problem. In multi-issue negotiation, with information available about the agents' preferences, a negotiation may result in a mutually beneficial agreement. In a competitive negotiation environment, however, self-interested agents may not be willing to reveal their preferences, and this can increase the difficulty of negotiating a mutually beneficial agreement. In order to solve this problem, this paper proposes a Bayesian-based approach which can help an agent to predict its opponent's preference in bilateral multi-issue negotiation. The proposed approach employs Bayesian theory to analyse the opponent's historical offers and to approximately predict the opponent's preference over negotiation issues. A counter-offer proposition algorithm is also integrated into the prediction approach to help agents to propose mutually beneficial offers based on the prediction results. Experimental results indicate good performance of the proposed approach in terms of utility gain and negotiation efficiency. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-issue negotiation Multi-agent system Opponent modelling preference prediction Bayesian learning

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Exploring Gaze-Based prediction Strategies for preference Detection in Dynamic Interface Elements 6

Exploring Gaze-Based Prediction Strategies for Preference De...

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6th ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR)

作者： Heck, Melanie Edinger, Janick Buenemann, Jonathan Becker, Christian Univ Mannheim Mannheim Germany Univ Hamburg Hamburg Germany

ISBN: (纸本)9781450380553

Digitization is currently infiltrating all daily processes, forcing casual computer users to become acquainted with unfamiliar tools. In order to avoid overstraining these users, simplified interfaces that are reduced to the functionality and content which are relevant to the individual user are imperative. Gaze-contingent systems thus monitor viewing behavior during natural system interactions to predict relevant interface elements. The prediction performance is highly dependent on the underlying features and algorithm, especially when the interface consist of dynamic elements such as videos. In this paper, we conduct two studies with a total of 233 subjects in which we record the viewers' gaze while watching videos. We then compare the quality of preference predictions for video elements of majority voting to the performance of machine learning. Our results indicate that (1) majority voting can predict preferences with an accuracy of up to 73% (66%) for two (four) elements, (2) machine learning improves the performance to 82% (74%), (3) prediction accuracy depends on the strength of the user's preference for an element, and (4) we can rank preferences for individual elements.

关键词： eye tracking adaptive user interfaces preference prediction

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E2EPref: An end-to-end preference-based framework for speech quality assessment to alleviate bias in direct assessment scores

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COMPUTER SPEECH AND LANGUAGE 2025年 93卷

作者： Hu, Cheng-Hung Yasuda, Yusuke Toda, Tomoki Nagoya Univ Furo ChoChikusa Ku Nagoya Aichi 4648601 Japan

In speech quality assessment (SQA), direct assessment (DA) scores are frequently used as the objective of model training. However, because the DA scores themselves have listener-wise bias and equal range bias, the scores predicted by models trained with DA scores do not always reflect the true quality score. In this study, we utilize preference-based learning for SQA by transforming the DA score prediction framework into a preference prediction framework. Our proposed End-to-End preference-based framework (E2EPref) for SQA is designed for predicting system-level quality scores directly. It contains four proposed components: pair generation, preference function, threshold selection, and preference aggregation. Through these functions of E2EPref, we aim to mitigate biases introduced by directly using DA scores for training. In experiments, we show that this framework helps the SQA model alleviate biases, resulting in higher system-level Spearman's rank correlation coefficient and linear correlation coefficient. Additionally, we evaluate the quality prediction capability of the framework in a zero-shot out-of-domain scenario. Finally, we collect subjective preference scores on a dataset already containing DA scores and analyze the advantages and disadvantages of using DA scores versus subjective preference scores as the ground truth or for model training.

关键词： Speech quality assessment preference prediction Direct assessment scores MOS

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Personalized Aesthetic Assessment: Integrating Fuzzy Logic and Color preferences

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IEEE ACCESS 2024年 12卷 97646-97663页

作者： Adilova, Ayana Shamoi, Pakizar Kazakh British Tech Univ Sch Informat Technol & Engn Alma Ata 050000 Kazakhstan

The analysis of aesthetic assessment is a complex and subjective task that has attracted researchers for a long time. The subjective nature of aesthetic preferences presents a significant challenge in defining and quantifying what makes images visually appealing. The current paper addresses this gap by introducing a novel methodology for quantifying and predicting aesthetic preferences in the case of interior design images. Our study combines fuzzy logic with image processing techniques. Firstly, a dataset of interior design images was collected from social media platforms, focusing on essential visual attributes such as color harmony, lightness, and complexity. Then, these features were integrated using a weighted average to compute a general aesthetic score. Our methodology considers personal color tastes when determining the overall aesthetic appeal. Initially, user feedback was collected on primary colors such as red, brown, and others to gauge their preferences. Subsequently, the image's five most prevalent colors were analyzed to determine the preferred color scheme based on pixel count. The color scheme preference and the aesthetic score are then passed as inputs to the fuzzy inference system to calculate an overall preference score. This score represents a comprehensive measure of the user's preference for a particular interior design, considering their color choices and general aesthetic appeal. The Two-Alternative Forced Choice (2AFC) method validated the methodology, resulting in a notable hit rate of 0.68. This study can help in fields such as art, design, advertising, or multimedia content creation, where aesthetic analysis and preference prediction are crucial. In the case of interior design, this study can help designers and professionals better understand and meet people's preferences, especially in a world that relies heavily on digital media.

关键词： Image color analysis Visualization Complexity theory Fuzzy logic Media Computational modeling Deep learning Social networking (online) Aesthetic preferences color harmony computational aesthetics fuzzy logic image processing interior design preference prediction social media

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