Decision Support Systems最新文献_第10页

Sentiment-aware cross-modal semantic interaction model for harmful meme detection 有害模因检测的情感感知跨模态语义交互模型

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-09-01 Epub Date: 2025-07-25 DOI: 10.1016/j.dss.2025.114509

Yuxiao Duan, Xiang Zhao, Hao Guo

The increasing proliferation of harmful memes has a serious negative impact on society, rendering the detection of such memes a formidable challenge. Prior research has predominantly concentrated on the modal and semantic attributes of memes while neglecting the significance of cross-modal interactions and detailed semantic information. Although some approaches have incorporated large language models, they often have the problem of harmful avoidance due to ethical constraints. To address these issues, we propose a novel sentiment-aware cross-modal semantic interaction detector, which delves into the profound implications through three principal dimensions: semantic extraction, modal interaction, and sentiment polarity assessment. In the semantic extraction module, Visual Question-Answering is utilized to incorporate detailed knowledge and descriptions. For modal interaction, the positional relationships between meme objects and texts are investigated, and a distance-based attentional multimodal detector is established. In the sentiment polarity module, the sentiment polarity of the text is judged. These components are integrated to form a cohesive joint detection system. Extensive experiments across three benchmark datasets demonstrate SSID significantly outperforms state-of-the-art baselines, enhancing detection accuracy and exhibiting robustness.

有害模因的日益泛滥对社会产生了严重的负面影响，对这些模因的检测是一项艰巨的挑战。以往的研究主要集中在模因的模态和语义属性上，而忽视了模因跨模态交互作用和详细语义信息的重要性。尽管一些方法结合了大型语言模型，但由于伦理约束，它们往往存在有害回避的问题。为了解决这些问题，我们提出了一种新的情感感知跨模态语义交互检测器，该检测器通过三个主要维度：语义提取、模态交互和情感极性评估来深入研究其深远影响。在语义提取模块中，采用可视化问答的方式，将详细的知识和描述融合在一起。对于模态交互，研究模因对象与文本之间的位置关系，建立基于距离的注意多模态检测器。在情感极性模块中，判断文本的情感极性。这些组件被整合成一个有凝聚力的联合检测系统。在三个基准数据集上进行的广泛实验表明，SSID显著优于最先进的基线，提高了检测精度并表现出鲁棒性。

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引用次数: 0

Modeling the role of generative AI in organizational privacy and security 生成式人工智能在组织隐私和安全中的作用建模

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-09-01 Epub Date: 2025-06-27 DOI: 10.1016/j.dss.2025.114500

Shweta Kumari Choudhary, Arpan Kumar Kar

In today's digital environment, organizations face security challenges like intentional breaches influenced by their specific policies and structures. As emerging technologies like Generative Artificial Intelligence (GAI) become more integrated into organizational processes, the adoption of GAI moderates organizational contextual conditions and rule characteristics, which affects the perceived risk of violating security rules. We extend the SOIPSV model to analyze cybersecurity practices and the strategic use of GAI in enhancing organizational resilience against security breaches. We establish the direct and moderating impacts of contextual conditions and rule characteristics, along with interactions in complex organizational cyber security. Our first study uses text mining for inferential and configurational analysis. Our second qualitative study explained the model of dynamic interplay between GAI and organizational factors. Our findings have implications for perceived risk management and managers redesigning business processes to manage security breaches.

在当今的数字环境中，组织面临着安全挑战，例如受其特定策略和结构影响的故意破坏。随着像生成式人工智能（GAI）这样的新兴技术越来越多地集成到组织流程中，GAI的采用缓和了组织的上下文条件和规则特征，这些条件和规则特征会影响违反安全规则的感知风险。我们扩展了SOIPSV模型，以分析网络安全实践和GAI在增强组织抵御安全漏洞方面的战略应用。我们建立了上下文条件和规则特征的直接和调节影响，以及复杂组织网络安全中的相互作用。我们的第一项研究使用文本挖掘进行推理和配置分析。我们的第二个定性研究解释了GAI与组织因素之间动态相互作用的模型。我们的发现对感知风险管理和管理人员重新设计业务流程以管理安全漏洞具有启示意义。

引用次数: 0

An explainable lesion detection transformer model for medical imaging diagnosis decision support: Design science research 用于医学影像诊断决策支持的可解释病变检测变压器模型：设计科学研究

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-09-01 Epub Date: 2025-06-16 DOI: 10.1016/j.dss.2025.114492

Xinwei Wang , Yi Feng , Sutong Wang , Dujuan Wang , T.C.E. Cheng

Utilizing machine learning methods for auxiliary decision support in medical imaging significantly reduces missed detections and unnecessary expenses. However, the strict accuracy and transparency requirements in the medical field pose challenges for deep learning applications based on neural networks. To address these issues, we propose a novel artificial intelligence artifact guided by the design science research methodology for lesion detection decision support in medical images, called Explainable Lesion DEtection TRansformer (EL-DETR). This approach features an explainable separate attention mechanism in the decoder that highlights the attention weights of content and location queries, providing insights into the inference process through attention mapping visualizations. In addition, we introduce a hybrid matching query strategy to enhance the learning of positive samples and develop an adaptive efficient compound loss function to optimize training. We demonstrate EL-DETR's superior accuracy, robustness, and interpretability using four real-world datasets, establishing it as a reliable tool for clinical diagnosis and treatment decision support based on medical imaging. The code and original data are available at https://github.com/weimingai/EL-DETR.

利用机器学习方法在医学成像中进行辅助决策支持，可以显著减少漏检和不必要的费用。然而，医学领域对准确性和透明度的严格要求给基于神经网络的深度学习应用带来了挑战。为了解决这些问题，我们提出了一种新的人工智能工件，以设计科学研究方法为指导，用于医学图像中的病变检测决策支持，称为可解释病变检测变压器（EL-DETR）。这种方法在解码器中具有可解释的独立注意机制，突出显示内容和位置查询的注意权重，通过注意映射可视化提供对推理过程的见解。此外，我们引入了一种混合匹配查询策略来增强正样本的学习，并开发了一种自适应的高效复合损失函数来优化训练。我们利用四个真实世界的数据集证明了EL-DETR优越的准确性、稳健性和可解释性，并将其建立为基于医学成像的临床诊断和治疗决策支持的可靠工具。代码和原始数据可在https://github.com/weimingai/EL-DETR上获得。

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引用次数: 0

More than meets the eye: Feature concerns and suggestions in mobile XR app reviews 手机XR应用评论中的功能关注点和建议

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-08-01 Epub Date: 2025-05-19 DOI: 10.1016/j.dss.2025.114475

Nohel Zaman , David M. Goldberg , Zhilei Qiao , Alan S. Abrahams

This study aims to address quality concerns in mobile Extended Reality (XR) apps by developing tools to classify user reviews from the Google Play Store. Our first major contribution is a holistic approach to coding thousands of reviews, offering a unified framework to assess feature concerns and feature suggestions, thereby enhancing the understanding of user expectations. We also introduce a novel toolset for extracting key terms related to these concerns and suggestions, facilitating rapid analysis and improvement of app functionalities. To validate our approach, we compare the performance of this automated coding method with sentiment analysis, deep learning-based BERT, and several large language models (LLMs). Additionally, our study extends the understanding of user satisfaction by analyzing quantitative indicators such as app ratings and review helpfulness, offering new perspectives on user perceptions of XR app quality. This research has discovered vital areas for feature improvement without implementing complex infrastructure and using advanced technical expertise, enabling app firms to better target their efforts in improving XR app experiences.

本研究旨在通过开发工具对b谷歌Play Store的用户评论进行分类，解决移动扩展现实（XR）应用的质量问题。我们的第一个主要贡献是对数千个评论进行编码的整体方法，提供了一个统一的框架来评估功能关注点和功能建议，从而增强了对用户期望的理解。我们还引入了一个新的工具集，用于提取与这些问题和建议相关的关键术语，促进快速分析和改进应用程序功能。为了验证我们的方法，我们将这种自动编码方法与情感分析、基于深度学习的BERT和几个大型语言模型（llm）的性能进行了比较。此外，我们的研究通过分析应用评级和评论帮助度等量化指标，扩展了对用户满意度的理解，为用户对XR应用质量的看法提供了新的视角。这项研究发现了在不实施复杂基础设施和使用先进技术专业知识的情况下进行功能改进的关键领域，使应用程序公司能够更好地瞄准改善XR应用程序体验的目标。

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引用次数: 0

Integrating temporal association rules into intelligent prediction system for metabolic dysfunction-associated fatty liver disease 将时间关联规则集成到代谢功能障碍相关脂肪肝疾病智能预测系统中

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-08-01 Epub Date: 2025-05-13 DOI: 10.1016/j.dss.2025.114467

Zhuoqing Wu , Chonghui Guo , Jingfeng Chen , Suying Ding , Yunchao Zheng

Healthcare big data provides trajectory data on chronic disease onset, progression, and outcomes, essential for understanding metabolic dysfunction-associated fatty liver disease (MAFLD) patient health dynamics. However, constructing explainable predictive models for MAFLD using longitudinal healthcare big data remains challenging due to its complexity. While several high-performance machine learning models have shown promise, their “black box” nature limits interpretability and trust among clinical healthcare professionals. Most studies also rely on cross-sectional data, which lacks the depth of longitudinal data, hindering accurate health status tracking. This paper proposes an intelligent MAFLD prediction system integrating temporal association rules (TARs) through a “human-in-the-loop” approach. By analyzing TARs that capture disease dynamics, the system incorporates high-quality domain knowledge into its predictive model. To enhance explainability, we use the SHapley Additive exPlanations framework alongside clinically significant TARs. The system’s effectiveness was validated on real-world data, showing improved MAFLD outcome prediction. Sensitivity analysis identified optimal TARs and robust model configurations. Finally, the online-deployed explainable prototype system demonstrates potential to boost trust and adoption among clinical healthcare professionals. Additionally, the system’s effectiveness and their willingness to use it were further evaluated through the “human-on-the-loop” method. These findings suggest the system could serve as a valuable tool for clinical applications and advance information systems design.

医疗保健大数据提供慢性疾病发病、进展和结果的轨迹数据，对于了解代谢功能障碍相关脂肪肝（MAFLD）患者的健康动态至关重要。然而，由于其复杂性，使用纵向医疗保健大数据构建可解释的MAFLD预测模型仍然具有挑战性。虽然一些高性能机器学习模型显示出了希望，但它们的“黑箱”性质限制了临床医疗保健专业人员的可解释性和信任度。大多数研究还依赖于横断面数据，缺乏纵向数据的深度，阻碍了准确的健康状况跟踪。本文通过“人在环”的方法，提出了一种集成时间关联规则（TARs）的MAFLD智能预测系统。通过分析捕捉疾病动态的TARs，系统将高质量的领域知识整合到其预测模型中。为了提高可解释性，我们将SHapley加性解释框架与临床显著的tar一起使用。在实际数据中验证了该系统的有效性，显示出改进的MAFLD结果预测。灵敏度分析确定了最优TARs和鲁棒模型配置。最后，在线部署的可解释原型系统展示了在临床医疗保健专业人员中提高信任和采用的潜力。此外，通过“人在循环”方法进一步评估了系统的有效性和他们使用它的意愿。这些发现表明该系统可以作为临床应用和先进信息系统设计的有价值的工具。

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引用次数: 0

Multi-interest transfer using contrastive learning for cross-domain recommendation 基于对比学习的多兴趣迁移跨领域推荐

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-08-01 Epub Date: 2025-05-23 DOI: 10.1016/j.dss.2025.114473

Yu-Lin Lai , Szu-Hao Huang , Chiao-Ting Chen , Cheng-Jhang Wu

With advancements in information technology, deep learning techniques have been widely applied to recommendation systems, substantially assisting businesses and users in making better decisions. However, it still faces some intractable limitations, such as the cold-start problem and data sparsity. Hence, cross-domain recommendations are proposed to address these problems by referring to the domains with richer data. Existing models usually apply domain- or user-level transferal to exchange information between domains. For domain-level transferals, information is transferred directly using a straightforward transformation without filtering. In contrast, user-level transferal sets trainable parameters to control the ratio of user embedding from two domains. The former is insufficiently precise for every user, and the latter encounters generalization issues. For these reasons, these methods ameliorate the cold-start problem but create a new problem: negative transfer. Thus, we propose an interest-level transferal called multi-interest transferal to more precisely extract multiple interests and transfer related ones according to the target items. Nevertheless, it is not easy to model interest correlations of different domains. We, therefore, devise three self-supervised learning tasks to model the correlations and extract discriminant information. The experimental results reveal that this model outperforms other state-of-the-art methods by about 7% to 10%. Through multi-interest and contrastive learning techniques, our approach can model the decision-making process more effectively in cross-domain recommendation.

随着信息技术的进步，深度学习技术已被广泛应用于推荐系统，极大地帮助企业和用户做出更好的决策。然而，它仍然面临一些棘手的限制，如冷启动问题和数据稀疏性。因此，为了解决这些问题，我们提出了跨领域的建议，即参考具有更丰富数据的领域。现有模型通常采用域级或用户级转移来交换域之间的信息。对于域级传输，信息直接使用直接转换而不进行过滤。相比之下，用户级转移设置可训练的参数来控制用户从两个域嵌入的比例。前者对每个用户都不够精确，而后者则遇到泛化问题。由于这些原因，这些方法改善了冷启动问题，但产生了一个新的问题：负转移。因此，我们提出了一种利益层面的转移，称为多利益转移，可以更精确地提取多个利益，并根据目标项目转移相关的利益。然而，对不同领域的兴趣相关性进行建模并不容易。因此，我们设计了三个自监督学习任务来建模相关性并提取判别信息。实验结果表明，该模型的性能优于其他最先进的方法约7%至10%。通过多兴趣和对比学习技术，我们的方法可以更有效地模拟跨领域推荐的决策过程。

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引用次数: 0

Social media meets FinTech platforms: How do online emotions support credit risk decision-making? 社交媒体与金融科技平台：网络情绪如何支持信用风险决策？

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-08-01 Epub Date: 2025-05-27 DOI: 10.1016/j.dss.2025.114471

Zenan Zhou , Zhichen Chen , Yingjie Zhang , Tian Lu , Xianghua Lu

As emerging FinTech platforms face pressure in efficiently managing credit risk, the human emotional spectrum of FinTech platform borrowers within social media becomes a potential source for gaining insight into and evaluating their financial behaviors. Collaborating with an Asian FinTech platform, we investigate the impact of social media emotions on a platform’s loan-approval decisions and repayment-reminder interventions before due dates. We demonstrate that anger at the pre-approval stage has a U-shaped relationship with platform borrowers’ default probability. We reveal what we call “a bright side of anger” with respect to curbing financial credit risk: moderate intensity of anger at the pre-approval stage suggests a lower loan default probability. We also find that the average happiness tendency of platform delinquent borrowers’ at the pre-maturity stage becomes informative and valuable, as it shows a U-shaped relationship with loan default; as for anger, it does not work therein. Furthermore, our field experiment indicates that a positive-expectation reminder is useful for prompting repayment when delinquent borrowers are in strong emotional intensities, regardless of anger or happiness. However, a negative-consequence reminder results in a higher default probability for delinquent borrowers who maintain high immediate happiness before the loan maturity dates. We draw on the classical appraisal theory of emotions and the feelings-as-information theory to interpret our findings. We offer non-trivial theoretical and practical implications to support FinTech platform credit risk decision-making by investigating the value of social media emotions and advocating for cross-functional coordination between debt approval and debt collection departments.

随着新兴金融科技平台在有效管理信用风险方面面临压力，社交媒体上金融科技平台借款人的人类情感谱成为了解和评估其金融行为的潜在来源。我们与一家亚洲金融科技平台合作，调查了社交媒体情绪对平台贷款审批决策和到期前还款提醒干预的影响。我们证明了预审批阶段的愤怒与平台借款人的违约概率呈u型关系。在抑制金融信贷风险方面，我们揭示了我们所谓的“愤怒的光明面”：在审批前阶段，适度的愤怒意味着较低的贷款违约概率。我们还发现，平台违约借款人在前期的平均幸福倾向与贷款违约呈u型关系，具有信息价值；至于愤怒，它在那里不起作用。此外，我们的实地实验表明，当拖欠借款人处于强烈的情绪强度时，无论是愤怒还是快乐，积极期望提醒都有助于促使还款。然而，对于在贷款到期日之前保持高即时幸福感的违约借款人来说，负后果提醒会导致更高的违约概率。我们利用经典的情绪评价理论和感觉作为信息理论来解释我们的发现。我们通过调查社交媒体情绪的价值，并倡导债务审批和债务催收部门之间的跨职能协调，为支持金融科技平台的信用风险决策提供了重要的理论和实践意义。

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引用次数: 0

Leveraging meta-path and co-attention to model consumer preference stability in fashion recommendations 利用元路径和共同关注来模拟时尚推荐中的消费者偏好稳定性

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-01 Epub Date: 2025-04-20 DOI: 10.1016/j.dss.2025.114455

Ya-Han Hu , Ting-Hsuan Liu , Kuanchin Chen , Fan-Chi Yeh

With countless outfit combinations available, consumers often experience choice overload. Two key challenges that significantly impact the quality of recommendation systems are recommendation accuracy and fluctuations in consumer preferences. Previous works primarily extracted generic product features and modeled the compatibility of fashion items, overlooking the relationships hidden in user-product interactions and the evolution of consumer preferences. Unfortunately, this evolution of consumer preferences has not received much attention in the RS studies. To address these limitations, we propose a GPA-BPR (General compatibility and Personalized preference with co-Attention mechanism) framework, which integrates multimodal insights for practical outfit evaluation and utilizes item-user-item meta-paths to capture consumers' stable preferences. Experiments demonstrate significant performance improvements. The co-attention mechanism in our framework effectively enhances recommendations based on meta-path contexts compared to similar previous studies.

由于有无数的服装组合可供选择，消费者经常会遇到选择过多的情况。影响推荐系统质量的两个关键挑战是推荐准确性和消费者偏好的波动。以往的研究主要是提取产品的共性特征，对时尚单品的兼容性进行建模，忽略了隐藏在用户-产品交互和消费者偏好演变中的关系。不幸的是，消费者偏好的这种演变在RS研究中没有得到太多关注。为了解决这些限制，我们提出了一个GPA-BPR（通用兼容性和个性化偏好与共同注意机制）框架，该框架集成了实用服装评估的多模式见解，并利用物品-用户-物品元路径来捕捉消费者的稳定偏好。实验证明了显著的性能改进。与之前类似的研究相比，我们框架中的共同注意机制有效地增强了基于元路径上下文的推荐。

引用次数: 0

Using large multimodal models to predict outfit compatibility 使用大型多模态模型预测服装兼容性

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-01 Epub Date: 2025-04-26 DOI: 10.1016/j.dss.2025.114457

Chia-Ling Chang , Yen-Liang Chen , Dao-Xuan Jiang

Outfit coordination is a direct way for people to express themselves. However, judging the compatibility between tops and bottoms requires considering multiple factors such as color and style. This process is time-consuming and prone to errors. In recent years, the development of large language models and large multi-modal models has transformed many application fields. This study aims to explore how to leverage these models to achieve breakthroughs in fashion outfit recommendations.

This research combines the keyword response text from the large language model Gemini in the Vision Question Answering (VQA) task with the deep feature fusion technology of the large multi-modal model Beit3. By providing only image data of the clothing, users can evaluate the compatibility of tops and bottoms, making the process more convenient. Our proposed model, the Large Multi-modality Language Model for Outfit Recommendation (LMLMO), outperforms previously proposed models on the FashionVC and Evaluation3 datasets. Moreover, experimental results show that different types of keyword responses have varying impacts on the model, offering new directions and insights for future research.

服装搭配是人们表达自己的一种直接方式。然而，判断上衣和下装的兼容性需要考虑多种因素，如颜色和款式。这个过程很耗时，而且容易出错。近年来，大型语言模型和大型多模态模型的发展改变了许多应用领域。本研究旨在探讨如何利用这些模型来实现时尚服装推荐的突破。本研究将视觉问答（VQA）任务中大型语言模型Gemini的关键字响应文本与大型多模态模型Beit3的深度特征融合技术相结合。通过提供服装的图像数据，用户可以评估上衣和下装的兼容性，使过程更加方便。我们提出的服装推荐大型多模态语言模型（LMLMO）在FashionVC和Evaluation3数据集上优于先前提出的模型。此外，实验结果表明，不同类型的关键词响应对模型的影响不同，为未来的研究提供了新的方向和见解。

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引用次数: 0

40 years of Decision Support Systems: A bibliometric analysis 决策支持系统的40年：文献计量学分析

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-01 Epub Date: 2025-05-08 DOI: 10.1016/j.dss.2025.114469

Li Guan, José M. Merigó, Ghassan Beydoun

Decision Support Systems (DSS) is a leading international journal dedicated to decision support system research and practice, with the aim of exploring theoretical and technical advancements to facilitate enhanced decision making in industry, commerce, government, and other business settings. The journal published its first issue in 1985, and in 2025, celebrates its 40th anniversary. Motivated by this special event, this paper develops a comprehensive bibliometric analysis to present a lifetime overview of the development characteristics and leading trends of DSS journal between 1985 and 2023. By using the bibliographic data collected from the Scopus and Web of Science Core Collection databases, this study analyzes the publication and citation structure of the journal and investigates a wide range of issues including the most cited papers, the most cited documents by the journal's publications, the citing articles, the most productive and influential authors, institutions and countries/territories, and the most popular keywords and topics. Moreover, this work also graphically maps the bibliographic material by using the visualization of similarities (VOS) viewer software. In the graphical analysis, several bibliometric techniques in terms of co-citation, bibliographic coupling, and co-occurrence of author keywords are adopted. The results accentuate the significant growth and impact of DSS journal throughout its lifetime. It is expected that the journal will continue to grow its international reputation and disseminate knowledge in decision support, information systems, and business area, providing an efficient mechanism for researchers around the world to keep abreast with advances in the scientific community.

决策支持系统（DSS）是一本致力于决策支持系统研究和实践的国际领先期刊，旨在探索理论和技术进步，以促进工业，商业，政府和其他商业环境中的决策制定。该杂志于1985年出版了第一期，并于2025年庆祝其40周年。基于这一特殊事件，本文采用文献计量分析方法，对1985 - 2023年DSS期刊的发展特征和主要趋势进行了全面的回顾。本研究利用Scopus和Web of Science Core Collection数据库收集的文献数据，分析了该期刊的出版和被引结构，调查了该期刊被引次数最多的论文、被引次数最多的文献、被引文章、最高产和最具影响力的作者、机构和国家/地区、最热门的关键词和主题等问题。此外，本工作还使用相似度可视化（VOS）查看器软件对书目材料进行图形化映射。在图形分析中，采用了共被引、书目耦合和作者关键词共现等文献计量学技术。这些结果突出了DSS期刊在其整个生命周期中的显著增长和影响。预计该杂志将继续提高其国际声誉，并在决策支持、信息系统和商业领域传播知识，为世界各地的研究人员提供与科学界进展同步的有效机制。

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引用次数: 0