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学者姓名：张文
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Abstract ：
This paper proposes a novel approach called Stack-GA(2)M to identify fraudulent reviewers in an inherently interpretable manner by fusing both target and non-target features. Specifically, for local interpretability, we adopt GA(2)M (Standard Generalized Additive Model plus interactions) as the basic classifier to produce three subordinate models trained by using the target features and the non-target features as review textual features and reviewer behavioral features. For global interpretability, we adopt LR (Logistic Regression) as the meta classifier to stack the outputs of three subordinate models to identify the fraudulent reviewers. The white-box model of LR enables us to understand the global interpretability of the target features and the non-target features in identifying fraudulent reviewers. With GA(2)M, the local interpretability of each subordinate model is derived by using feature importance, spline shape functions for individual features, and heatmaps for interaction terms. Extensive experiments on Yelp dataset demonstrate that the proposed Stack-GA(2)M approach is superior to state-of-the-art techniques in identifying fraudulent reviewers and exhibits favorable inherent interpretability.
Keyword ：
stacking model stacking model Fraudulent reviewer detection Fraudulent reviewer detection standard generalized additive model standard generalized additive model interpretable machine learning interpretable machine learning
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| GB/T 7714 | Zhang, Wen , Zhang, Xuan , Chen, Jindong et al. Stacking GA2M for inherently interpretable fraudulent reviewer identification by fusing target and non-target features [J]. | INTERNATIONAL JOURNAL OF GENERAL SYSTEMS , 2024 . |
| MLA | Zhang, Wen et al. "Stacking GA2M for inherently interpretable fraudulent reviewer identification by fusing target and non-target features" . | INTERNATIONAL JOURNAL OF GENERAL SYSTEMS (2024) . |
| APA | Zhang, Wen , Zhang, Xuan , Chen, Jindong , Li, Jian , Ma, Zhenzhong . Stacking GA2M for inherently interpretable fraudulent reviewer identification by fusing target and non-target features . | INTERNATIONAL JOURNAL OF GENERAL SYSTEMS , 2024 . |
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Abstract ：
It is crucial to predict the credit risk of small and medium-sized enterprises (SMEs) accurately for the success of supply chain finance (SCF). However, most of the existing research ignore the fact that the data distribution is usually imbalanced, that is, the proportion of default SMEs is much smaller than that of non-default SMEs. To fill this research gap, we propose a novel approach called DRL-Risk to deal with the imbalanced credit risk prediction (ICRP) of SMEs in SCF with deep reinforcement learning (DRL). Specifically, we formulate the ICRP problem as a Markov decision process and suggest an instance-based reward function to incorporate financial loss into the reward function with consideration of the actual loss caused by misclassification in the ICRP of SMEs. Then, we recommend a deep dueling neural network for decision policy to predict the credit risk of SMEs. With deep reinforcement learning, the DRL-Risk approach can prioritize the learning on the SMEs that would lead to great financial losses. Experimental results demonstrate that the DRL-Risk approach can significantly improve the performance of credit risk prediction of SMEs in SCF compared with the baseline methods in recall, G-mean, and financial loss. We have also identified management implications for the decision-makers participating in SCF.
Keyword ：
Financial loss Financial loss DRL-risk DRL-risk Imbalanced credit risk prediction Imbalanced credit risk prediction Instance-based reward Instance-based reward Deep reinforcement learning Deep reinforcement learning
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| GB/T 7714 | Zhang, Wen , Yan, Shaoshan , Li, Jian et al. Deep reinforcement learning imbalanced credit risk of SMEs in supply chain finance [J]. | ANNALS OF OPERATIONS RESEARCH , 2024 . |
| MLA | Zhang, Wen et al. "Deep reinforcement learning imbalanced credit risk of SMEs in supply chain finance" . | ANNALS OF OPERATIONS RESEARCH (2024) . |
| APA | Zhang, Wen , Yan, Shaoshan , Li, Jian , Peng, Rui , Tian, Xin . Deep reinforcement learning imbalanced credit risk of SMEs in supply chain finance . | ANNALS OF OPERATIONS RESEARCH , 2024 . |
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Abstract ：
Online question-and-answer communities are seriously threatened by low user participation. There is currently a rare comprehensive study on the knowledge contribution pattern of consistently active participants and the moderating role of peer recognition, which can help improve low participation and reengage inactive users, despite researchers having examined the various facets of knowledge contribution and made helpful suggestions. As per the self-determination and social cognitive theory, the communal environment impacts peers and imitates role models or reliable sources in their involvement patterns. We have examined StackOverflow's most reliable active users from 2010 to 2020 using the social cognition and self-determination theories to use the findings to reactivate dormant users. We have used a two-step dynamic system GMM model to get robust and reliable findings. The research discovered that peer repudiation, reputation, and online social interactions favorably affect the contributed knowledge. However, knowledge-seeking and earning virtual badges such as gold and bronze usually negatively impact it. Furthermore, it was revealed that the effect of virtual badges on contributed knowledge was positively moderated by peer recognition. However, peer recognition reduces the benefits of social interaction and reputation on the contributed knowledge. The study's findings advance the body of knowledge and provide thorough management implications for raising low participation, reengaging inactive users, and cultivating a culture of innovative sharing of knowledge.
Keyword ：
Low participation Low participation Active participants Active participants Peer recognition Peer recognition Dormant users Dormant users Q&A communities Q&A communities
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| GB/T 7714 | Mustafa, Sohaib , Zhang, Wen , Naveed, Muhammad Mateen et al. Using social Cognitive theory to reengage dormant users in question and answer Communities: A case study of active StackOverflow participants [J]. | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2024 , 68 . |
| MLA | Mustafa, Sohaib et al. "Using social Cognitive theory to reengage dormant users in question and answer Communities: A case study of active StackOverflow participants" . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 68 (2024) . |
| APA | Mustafa, Sohaib , Zhang, Wen , Naveed, Muhammad Mateen , Adan, Dur e . Using social Cognitive theory to reengage dormant users in question and answer Communities: A case study of active StackOverflow participants . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2024 , 68 . |
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Abstract ：
Recent years have witnessed an increasing number of manipulated online reviews in e-commerce platforms. Previous research has provided substantial evidence that vendor manipulation of online reviews has a significant negative impact on the stakeholders involved in the e-commerce business. Many platforms take various governance measures to filter manipulated reviews. Nevertheless, the effectiveness of these measures still remains unknown to a large extent. To bridge this research gap, this paper investigates the effect of differentiated platform governance, including defined as interventions to counterattack manipulation intensity, manipulation duration, and perceived quality manipulated, on the probability of future review manipulation. We develop a game theoretical model that incorporates the strategic interactions between the platform and vendors, which yield several testable hypotheses. We then conduct an empirical analysis of platform governance and review manipulation by using the review manipulation data collected from Amazon.com. Results of the analytical model and empirical analysis show that platform governance that targets manipulation intensity and manipulation duration can both effectively mitigate review manipulation probability. On the contrary, platform governance to counterattack manipulating perceived product quality exhibits an inverted U-shape relationship with review manipulation probability. This study provides novel insights into how to better mitigate online review manipulation for e-commerce platforms.
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| GB/T 7714 | Wang, Qiang , Zhang, Wen , Li, Jian et al. The devil is in the details! Effect of differentiated platform governance on online review manipulation [J]. | HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS , 2024 , 11 (1) . |
| MLA | Wang, Qiang et al. "The devil is in the details! Effect of differentiated platform governance on online review manipulation" . | HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS 11 . 1 (2024) . |
| APA | Wang, Qiang , Zhang, Wen , Li, Jian , Mai, Feng , Ma, Zhenzhong . The devil is in the details! Effect of differentiated platform governance on online review manipulation . | HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS , 2024 , 11 (1) . |
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Abstract ：
The online review fraud process is an event in which a reviewer posts fraudulent reviews on an e-commerce platform with respect to a review target, such as a commodity or service. Extant studies on detecting fraudulent reviewers often rely on reviewer behavioral patterns and the textual content of reviews while ignoring the targets being reviewed. Based on the Goals-Plans-Action theory, we examine the relative importance of target features for fraudulent reviewer detection in comparison to that of non-target features. Target features refer to the features derived from the innate information related to the reviewed products or services while non-target features refer to the features derived from the acquired information related to the reviewed products or services. In this study, we analyze a sample from the Yelp.com dataset of restaurant reviews to help better understand the importance of target features and non-target features. The results suggest that using the combination of target features with non-target features can improve the performance of online fraudulent reviewer detection in comparison with using non-target features alone. Moreover, using the combination of target features with nontarget features will further improve the performance of online fraudulent reviewer detection when we consider the non-target features as conditioned on the reviewed target features rather than treating them as independent of each other.
Keyword ：
Online review fraud Online review fraud Goals -Plans -Action theory Goals -Plans -Action theory Non -target features Non -target features Fraudulent reviewer detection Fraudulent reviewer detection Target features Target features
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| GB/T 7714 | Wang, Qiang , Zhang, Wen , Li, Jian et al. Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection [J]. | JOURNAL OF BUSINESS RESEARCH , 2023 , 167 . |
| MLA | Wang, Qiang et al. "Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection" . | JOURNAL OF BUSINESS RESEARCH 167 (2023) . |
| APA | Wang, Qiang , Zhang, Wen , Li, Jian , Ma, Zhenzhong . Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection . | JOURNAL OF BUSINESS RESEARCH , 2023 , 167 . |
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| GB/T 7714 | Zhang, Wen , Xu, Xiaofeng , Wu, Jun et al. Preface to the Special Issue on "Computational and Mathematical Methods in Information Science and Engineering" [J]. | MATHEMATICS , 2023 , 11 (14) . |
| MLA | Zhang, Wen et al. "Preface to the Special Issue on "Computational and Mathematical Methods in Information Science and Engineering"" . | MATHEMATICS 11 . 14 (2023) . |
| APA | Zhang, Wen , Xu, Xiaofeng , Wu, Jun , He, Kaijian . Preface to the Special Issue on "Computational and Mathematical Methods in Information Science and Engineering" . | MATHEMATICS , 2023 , 11 (14) . |
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Abstract ：
Complete data on wastewater quality are essential for managing and monitoring wastewater treatment processes. Most management and monitoring methods involve the use of voluminous training data for imputation, but the problem is that the sensors used in wastewater treatment plants (WWTPs) collect only a limited amount of data. The lack of sufficient training data can diminish the accuracy of traditional imputation techniques. To address this problem, this study developed a novel approach called Miss-GBRT (imputing missing values with gradient boosting regression trees), which can impute missing values into wastewater quality data even with minimal training data. The proposed approach consists of a preprocessing stage and an imputation stage. In the preprocessing stage, different copies of masked datasets are produced from raw data according to various levels of missingness, after which pre-imputation is conducted to ensure the integrality of training data. In the imputation stage, Miss-GBRT is used to combine shallow regression trees to regress the residuals of time and impute each missing value into a masked dataset in a stepwise manner. We carried out extensive experiments on the WWTP datasets of the University of California, Irvine and Beijing Drainage Group to compare Miss-GBRT with baseline imputation methods. The results demonstrated that the proposed approach improves the accuracy with which missing wastewater quality data are imputed under limited training data. It can also perform better than other methods on datasets with considerable proportions of missing values.
Keyword ：
Missing imputation Missing imputation WWTPs WWTPs Wastewater quality data Wastewater quality data Limited data Limited data Miss-GBRT Miss-GBRT
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| GB/T 7714 | Zhang, Wen , Li, Rui , Zhao, Jiangpeng et al. Miss-gradient boosting regression tree: a novel approach to imputing water treatment data [J]. | APPLIED INTELLIGENCE , 2023 , 53 (19) : 22917-22937 . |
| MLA | Zhang, Wen et al. "Miss-gradient boosting regression tree: a novel approach to imputing water treatment data" . | APPLIED INTELLIGENCE 53 . 19 (2023) : 22917-22937 . |
| APA | Zhang, Wen , Li, Rui , Zhao, Jiangpeng , Wang, Jiawei , Meng, Xiaoyu , Li, Qun . Miss-gradient boosting regression tree: a novel approach to imputing water treatment data . | APPLIED INTELLIGENCE , 2023 , 53 (19) , 22917-22937 . |
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Abstract ：
In recent years, there has been an increase in online review manipulation on the platforms of electronic commerce. Previous studies clarify the benefits and harms of online review manipulation for firms, and they provide mixed conclusions on the influence of online review manipulation. However, the effect of online review manipulation on product sales has not yet been thoroughly studied due to the covert nature of review manipulation. To fill this research gap, this paper examines the different effects of review manipulation in three dimensions: quantity manipulation, quality manipulation, and relation manipulation. Drawing on the Information Manipulation Theory, which reveals the manipulation behaviors of different dimensions, it is proposed that the influence of online review manipulation differs significantly among different information manipulation dimensions. The results of the empirical experiments show that the effect of review quantity manipulation on product sales exhibits an inverted U-shape. In addition, review quality manipulation positively affects product sales, but review relation manipulation exerts a negative effect. Moreover, the magnitude of the effect of review manipulation is contingent upon review manipulation duration. The findings shed light on the heterogeneous effect of review manipulation dimensions on product sales from an information manipulation perspective and suggest a need for improvement in online fraudulent review detection in the early stage of review manipulation.
Keyword ：
Online review manipulation Online review manipulation Quantity manipulation Quantity manipulation Quality manipulation Quality manipulation Review manipulation duration Review manipulation duration Relation manipulation Relation manipulation
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| GB/T 7714 | Wang, Qiang , Zhang, Wen , Li, Jian et al. Benefits or harms? The effect of online review manipulation on sales [J]. | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2023 , 57 . |
| MLA | Wang, Qiang et al. "Benefits or harms? The effect of online review manipulation on sales" . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 57 (2023) . |
| APA | Wang, Qiang , Zhang, Wen , Li, Jian , Ma, Zhenzhong , Chen, Jindong . Benefits or harms? The effect of online review manipulation on sales . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2023 , 57 . |
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Abstract ：
Accurate traffic congestion prediction is crucial for efficient urban intelligent transportation systems (ITS). Though most existing methods attempt to characterize spatial correlation and temporal correlation in traffic congestion, few of them consider spatial heterogeneity and temporal heterogeneity: spatial correlation depends on temporality, and temporal correlation depends on spatiality in traffic congestion. To address this problem, this paper proposes a novel approach called TCP-BAST with bilateral alternation to simultaneously capture both the correlation and the heterogeneity between spatiality and temporality to improve traffic congestion prediction. First, to capture spatial correlation and spatial heterogeneity, we propose a spatial–temporal alternation (STA) module with multi-head graph attention networks and temporal embedding. Second, to capture temporal correlation and temporal heterogeneity, we propose a temporal-spatial alternation (TSA) module with multi-head masked attention networks and spatial embedding. Third, to predict the traffic congestion of multiple road sections in a traffic network, we propose a spatial–temporal fusion (STF) module to fuse the multi-grained spatial-temporal features derived from the STA and TSA modules. The experimental results on a real-world traffic dataset demonstrate that the proposed TCP-BAST approach outperforms the baseline methods in terms of both the mean absolute error (MAE) and the root mean squared error (RMSE). Both spatial-temporal alternation and temporal-spatial alternation are important for improving traffic congestion prediction, with the former being more critical than the latter. © 2022 Elsevier Inc.
Keyword ：
Bilateral alternation; Spatial heterogeneity; Spatial-temporal fusion; Temporal heterogeneity; Traffic congestion prediction Bilateral alternation; Spatial heterogeneity; Spatial-temporal fusion; Temporal heterogeneity; Traffic congestion prediction
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| GB/T 7714 | Zhang, W. , Yan, S. , Li, J. . TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality [J]. | Information Sciences , 2022 , 608 : 718-733 . |
| MLA | Zhang, W. et al. "TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality" . | Information Sciences 608 (2022) : 718-733 . |
| APA | Zhang, W. , Yan, S. , Li, J. . TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality . | Information Sciences , 2022 , 608 , 718-733 . |
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Abstract ：
The credit risk of small and medium-sized enterprises (SMEs) in supply chain finance (SCF) is defined as the probability that the SME would default on loans derived from financing for the SCF platform. Traditional models make use of merely the static data of SMEs, such as enterprise demographic data and financial statement data, to predict the credit risk of SMEs in SCF. Nevertheless, behavioral data, which reflect the dynamic financing behavior of SMEs in SCF, are overlooked by these models, which limits the performance of credit risk prediction. To address this problem, a novel approach is proposed called DeepRisk to fuse enterprise demographic data and financing behavioral data to predict the credit risk of SMEs in SCF. We adopt the multi-modal learning strategy to fuse the two different sources of data. The concatenated vectors derived from data fusion are then used as the input of the feed forward neural network to predict the credit risk of SMEs. Experiments on a real SCF dataset demonstrate that the proposed DeepRisk approach outperforms the baseline methods in credit risk prediction in terms of precision, recall, F1-score, area under curve (AUC), and economic loss. The fusion of the two different sources of data is superior to the existing approaches to the credit risk prediction of SMEs in SCF. Both the static enterprise demographic data and the dynamic financing behavioral data are crucial to improve the credit risk prediction of SMEs. Nevertheless, the variables derived from the financing behavioral data have a better predictability than those from the enterprise demographic data. Managerial implications have been identified for decision makers involved in SCF in utilizing the benefits of SCF and in managing their credit risks.
Keyword ：
Credit risk prediction Credit risk prediction DeepRisk DeepRisk Supply chain finance Supply chain finance Data fusion Data fusion Multi-modal learning Multi-modal learning
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| GB/T 7714 | Zhang, Wen , Yan, Shaoshan , Li, Jian et al. Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data [J]. | TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW , 2022 , 158 . |
| MLA | Zhang, Wen et al. "Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data" . | TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 158 (2022) . |
| APA | Zhang, Wen , Yan, Shaoshan , Li, Jian , Tian, Xin , Yoshida, Taketoshi . Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data . | TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW , 2022 , 158 . |
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