检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Qiu, Ju Zhao, Ju Hu, Xiaojian Zhou, Yongwu Min, Jie School of Management Hefei University of Technology Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China School of Business Administration South China University of Technology Guangdong Guangzhou510640 China Department of Mathematics Anhui Jianzhu University Anhui Hefei230601 China

This paper investigates a newly emerging recommendation strategy for online niche products, that is, a multichannel retailer recommends online niche products for instore shoppers. Such cross-channel recommendations make more customers aware of niche products, generating a positive information effect for the online channel. It also increases the interaction between online and offline channels. However, it is unclear how the provision of cross-channel recommendations influences the channel relationship when online and offline channels sell common products. Our analysis reveals that the cross-channel recommendation can increase the offline price and sales of common products, which benefits the offline channel. But the online sales of common products are decreased, causing a cannibalization effect on the online channel. The negative effect on common products can be dominated by the positive information effect on niche products when recommending a high-price niche product. In such case, the online channel benefits from cross-channel recommendations and channel synergy is achieved. From the perspective of a multichannel retailer, recommending high-price niche products to in-store shoppers can increase the retailer’s overall profit although the market for common product might shrink. Focusing on the target market with a high potential market scale of recommended customers and increasing the recommendation precision can make cross-channel recommendations more effective. © 2022, The Authors. All rights reserved.

关键词： Sales

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Impacts of Cross-Channel Recommendation for Online-Niche Products

SSRN

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SSRN 2022年

关键词： Sales

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Emotion-aware Personalized Music Recommendation with a Heterogeneity-aware Deep Bayesian Network

arXiv

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arXiv 2024年

作者： Jing, Erkang Liu, Yezheng Chai, Yidong Yu, Shuo Liu, Longshun Jiang, Yuanchun Wang, Yang School of Management Hefei University of Technology Anhui Hefei China School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Anhui Hefei China School of Management Hefei University of Technology Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management Anhui Hefei China Rawls College of Business Texas Tech University LubbockTX United States School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China

Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users’ actual emotional states expressed through identical emotional words are homogeneous. They also assume that users’ music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user’s decision process of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://***/jingrk/HDBN. Copyright © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Probabilistic Runoff Forecasting Considering Stepwise Decomposition Framework and External Feature Integration Structure

SSRN

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SSRN 2023年

作者： Cao, Chaojin He, Yaoyao Cai, Siyu School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology Ministry of Education Hefei230009 China Anhui Key Laboratory of Philosophy and Social Sciences of Energy and Environment Smart Management and Green Low Carbon Development Hefei University of Technology Hefei230009 China China Institute of Water Resources and Hydropower Research Beijing100038 China

As dramatic weather changes complexify the change of runoff and introduce significant uncertainties, probabilistic runoff forecasting becomes increasingly important for water resource managers because it can quantify the uncertainty in the prediction process. However, little research has been conducted on how to employ decomposition techniques rationally in probabilistic prediction while effectively considering external factors. To fill the gap, this paper proposes a novel framework for probabilistic runoff forecasting, which incorporates several key components. It first utilizes the stepwise decomposition (SD) technique to prevent the information leakage problem. Then an external feature integration structure is designed to enhance the learning ability of the neural network for various features and decomposition sequences. The boundary correction technique is implemented to mitigate the boundary distortion problem. This comprehensive framework is named SDIFC. Building upon this framework, we achieve a synergistic integration of three distinct methods: seasonal-trend decomposition procedures based on loess (STL), least absolute shrinkage and selection operator (LASSO), and quantile regression long short-term memory (QRLSTM). This method is referred to as STL-LASSO-QRLSTM. We employ two datasets from the Lutaizi and Wangjiaba stations in China to conduct case studies. Several hybrid models and comparison frameworks are developed to conduct adequate comparative experiments, which are evaluated using six metrics that measure deterministic and probabilistic performance. The findings show that the predictive power of the proposed model exceeds comparative methods based on the normal and SD frameworks. This research holds immense importance for developing probabilistic models based on deep learning for runoff forecasting. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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SFF-DA: Sptialtemporal Feature Fusion for Detecting Anxiety Nonintrusively

arXiv

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arXiv 2022年

作者： Mo, Haimiao Li, Yuchen Yang, Shanlin Zhang, Wei Ding, Shuai The School of Management Hefei University of Technology Anhui Hefei23009 China The Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education China The National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200135 China The West China Hospital of Sichuan University Sichuan Chengdu610041 China

Early detection of anxiety is crucial for reducing the suffering of individuals with mental disorders and improving treatment outcomes. Utilizing an mHealth platform for anxiety screening can be particularly practical in improving screening efficiency and reducing costs. However, the effectiveness of existing methods has been hindered by differences in mobile devices used to capture subjects' physical and mental evaluations, as well as by the variability in data quality and small sample size problems encountered in real-world settings. To address these issues, we propose a framework with spatiotemporal feature fusion for detecting anxiety nonintrusively. We use a feature extraction network based on a 3D convolutional network and long short-term memory ("3DCNN+LSTM") to fuse the spatiotemporal features of facial behavior and noncontact physiology, which reduces the impact of uneven data quality. Additionally, we design a similarity assessment strategy to address the issue of deteriorating model accuracy due to small sample sizes. Our framework is validated with a crew dataset from the real world and two public datasets: the University of Burgundy Franche-Comté Psychophysiological (UBFC-Phys) dataset and the Smart Reasoning for Well-being at Home and at Work for Knowledge Work (SWELL-KW) dataset. The experimental results indicate that our framework outperforms the comparison methods. © 2022, CC BY-NC-SA.

关键词： mHealth

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Cost-Effective Community-Hierarchy-Based Mutual Voting Approach for Influence Maximization in Complex Networks

arXiv

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arXiv 2024年

作者： Liu, Yi Tang, Xiaoan Pedrycz, Witold Zhang, Qiang The Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education The School of Management Hefei University of Technology Hefei Box 270 Anhui Hefei230009 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey

Various types of promising techniques have come into being for influence maximization whose aim is to identify influential nodes in complex networks. In essence, real-world applications usually have high requirements on the balance between time complexity and accuracy of influential nodes identification. To address the challenges of imperfect node influence measurement and inefficient seed nodes selection mechanism in such class of foregoing techniques, this article proposes a novel approach called Cost-Effective Community-Hierarchy-Based Mutual Voting for influence maximization in complex networks. First, we develop a method for measuring the importance of different nodes in networks based on an original concept of Dual-Scale Community-Hierarchy Information that synthesizes both hierarchy structural information and community structural information of nodes. The community structural information contained in the nodes is measured by a new notion of Hierarchical-Community Entropy. Second, we develop a method named Cost-Effective Mutual-Influence-based Voting for seed nodes selection. Hereinto, a low-computational-cost mutual voting mechanism and an updating strategy called Lazy Score Updating Strategy are newly constructed for optimizing the selecting of seed nodes. Third, we develop a balance index to evaluate the performance of different methods in striking the tradeoff between time complexity and the accuracy of influential nodes identification. Finally, we demonstrate the approach performance over ten public datasets. The extensive experiments show that the proposed approach outperforms 16 state-of-the-art techniques on the balance between time complexity and accuracy of influential nodes identification. Compared with the method with the second highest value of the balance index, our approach can be improved by at most 9.29%. Copyright © 2024, The Authors. All rights reserved.

关键词： Expectation maximization algorithm

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Data-driven group decision making for diagnosis of thyroid nodule

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Science China(Information Sciences) 2019年第11期62卷 151-173页

作者： Chao FU Wenjun CHANG Weiyong LIU Shanlin YANG School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Department of Ultrasound The First Affiliated Hospital of USTC Division of Life Sciences and MedicineUniversity of Science and Technology of China

Emerging information technologies' integration into various fields has enhanced the development of these fields. Large volumes of data have been accumulated in this process. The accumulated data offer opportunities and challenges for people facing practical problems. On the one hand, it is essential to depend on a group's capabilities rather than an individual's capabilities to handle practical problems because the individual may lack sufficient expertise and experience to use data. In this situation, the practical problems can be considered as group decision making(GDM) problems. On the other hand, the accumulated data can help generate quality solutions to GDM problems. To obtain such solutions under the assumption that the accumulated data regarding a specific decision problem are available, this paper proposes a data-driven GDM method. In the method, decision makers' weights are learned from historical overall assessments and the corresponding gold standards, while criterion weights are learned from historical overall assessments and the corresponding decision matrices. The learned expert weights and criterion weights are used to produce the aggregated assessments, from which alternatives are compared or the overall conclusion is made. In a tertiary hospital located in Hefei, Anhui Province, China, the proposed method is applied to aid radiologists in diagnosing thyroid nodules.

关键词： data-driven group decision making interval number learning of expert weights learning of criterion weights diagnosis of thyroid nodule

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Understanding Continued Use Intention of AI Assistants

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Data processor for Better Business Education 2023年第6期63卷 1424页

作者： Yuguang Xie Shuping Zhao Peiyu Zhou Changyong Liang a Hefei University of Technology Hefei China a Hefei University of Technology Hefei Chinab Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education Hefei China

ABSTRACTIn recent years, smart home assistants have been used by a large number of people due to their simple, hands-free, voice-based operation. To ensure the long-term success and widespread dissemination of a product, it is important to evaluate its continued use. This study is mainly based on uses and gratifications theory to explore the relationship between the initial use of, gratification provided by, and continued use intention of smart home assistants, and to analyze differences in use by different age groups. The results confirm that different types of SHAs use lead to different levels of gratification in different categories. And gratification of different categories of users has a significant positive impact on the continued use intention. In addition, significant differences exist in the impact path of using smart home assistants to alleviate loneliness, among different age groups.

关键词： Human-computer interaction smart home assistants continued use intention U&G theory age differences

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An Intuitionistic Fuzzy Similarity Measurement Method Considering Orientation

An Intuitionistic Fuzzy Similarity Measurement Method Consid...

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作者： PEI Feng HE Yuwei YAN An LIU Lin School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision Making Ministry of Education

Similarity of intuitionistic fuzzy sets(IFSs) is an important tool for determining the similarity degree between two objects,which is often used in the fields of pattern recognition,medical diagnosis and so *** many intuitionistic fuzzy similarity measurement(IFSM) methods have been proposed,those methods ignore the effect of the hesitancy part's orientation on similarity degree,which causes partial information loss and reduces the reasoning *** this paper,an improved IFSM method considering the orientation of hesitancy part on membership part,non-membership part and hesitancy part,is established by introducing the protection degrees of membership,non-membership and ***,this method is applied to pattern recognition problems to demonstrate the effectiveness and applicability of the proposed method.

关键词： Intuitionistic fuzzy sets similarity measurement method orientation pattern recognition

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Photovoltaic Power Forecasting Based on SVM Optimized by Improved ABC

Photovoltaic Power Forecasting Based on SVM Optimized by Imp...

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作者： Lin Liu Yanyun Lu Shuqin Li School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education

In order to improve the accuracy of photovoltaic power output prediction, a photovoltaic power prediction method based on similar days and improved artificial bee colony support vector machine is proposed. Firstly, through calculating the Euclidean distance of history day and measured day meteorological factors to determine similar days. Secondly, select historical data of photovoltaic power output, temperature, humidity and daily radiation on the slope of similar days and temperature, humidity and daily radiation on the slope of test date as input variables of support vector machine. And we adopt the improved artificial bees colony to optimize kernel function parameters and the penalty factor of support vector machine. Finally get the output in each period of photovoltaic power prediction. The experimental results showed that the proposed method can effectively improve the prediction accuracy of photovoltaic power.

关键词： Photovoltaic Power Forecasting Based on SVM Optimized by Improved ABC

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