Pub Date : 2025-12-01Epub Date: 2025-10-06DOI: 10.1016/j.dss.2025.114553
Yuwei Wan , Zheyuan Chen , Ying Liu , Chong Chen , Michael Packianather
Translating natural language inquiries into executable Cypher queries (text-to-Cypher) is a persistent bottleneck for non-technical teams relying on knowledge graphs (KGs) in fast-changing industrial settings. Rule and template converters need frequent updates as schemas evolve, while supervised and fine-tuned parsers require recurring training. This study proposes a schema-guided prompting approach, namely text-to-Cypher with semantic schema (T2CSS), to align large language models (LLMs) with domain knowledge for producing accurate Cypher. T2CSS distils a domain ontology into a lightweight semantic schema and uses adaptive filtering to inject the relevant subgraph and essential Cypher rules into the prompt for constraining generation and reducing schema-agnostic errors. This design keeps the prompt focused and within context length limits while providing the necessary domain grounding. Comparative experiments demonstrate that T2CSS with GPT-4 outperformed baseline models and achieved 86 % accuracy in producing correct Cypher queries. In practice, this study reduces retraining and maintenance effort, shortens turnaround times, and broadens KG access for non-experts.
{"title":"Prompting large language models based on semantic schema for text-to-Cypher transformation towards domain Q&A","authors":"Yuwei Wan , Zheyuan Chen , Ying Liu , Chong Chen , Michael Packianather","doi":"10.1016/j.dss.2025.114553","DOIUrl":"10.1016/j.dss.2025.114553","url":null,"abstract":"<div><div>Translating natural language inquiries into executable Cypher queries (text-to-Cypher) is a persistent bottleneck for non-technical teams relying on knowledge graphs (KGs) in fast-changing industrial settings. Rule and template converters need frequent updates as schemas evolve, while supervised and fine-tuned parsers require recurring training. This study proposes a schema-guided prompting approach, namely text-to-Cypher with semantic schema (T2CSS), to align large language models (LLMs) with domain knowledge for producing accurate Cypher. T2CSS distils a domain ontology into a lightweight semantic schema and uses adaptive filtering to inject the relevant subgraph and essential Cypher rules into the prompt for constraining generation and reducing schema-agnostic errors. This design keeps the prompt focused and within context length limits while providing the necessary domain grounding. Comparative experiments demonstrate that T2CSS with GPT-4 outperformed baseline models and achieved 86 % accuracy in producing correct Cypher queries. In practice, this study reduces retraining and maintenance effort, shortens turnaround times, and broadens KG access for non-experts.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"199 ","pages":"Article 114553"},"PeriodicalIF":6.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-06DOI: 10.1016/j.dss.2025.114554
Xingchen Chen , Hao Liu , Libo Liu , Kristijan Mirkovski , Marta Indulska , Katja Holtta-Otto
Customer-driven innovation relies on leveraging customer insights to develop or improve products that meet evolving customer needs and preferences. Central to this innovation is the ideation process that involves two key stages: identifying customer needs and generating new ideas. While user-generated content offers a rich source of consumer insights, existing approaches for automating the ideation process—including unsupervised learning, supervised learning, deep learning, text summarization and GenAI—face limitations that restrict their scalability and practical utility. Moreover, these approaches often address only isolated stages of the ideation process. Based on a design science methodology and grounded in the user innovation theory, this paper develops and evaluates an integrated GenAI-driven method that automates the ideation process. The method consists of two stages: (1) customer opinion knowledgebase construction and (2) GenAI-based idea generation. The proposed GenAI-driven method offers an adaptable, scalable, and comprehensive solution for advancing customer-driven innovation.
{"title":"An integrated GenAI-driven method for automating ideation with user-generated content","authors":"Xingchen Chen , Hao Liu , Libo Liu , Kristijan Mirkovski , Marta Indulska , Katja Holtta-Otto","doi":"10.1016/j.dss.2025.114554","DOIUrl":"10.1016/j.dss.2025.114554","url":null,"abstract":"<div><div>Customer-driven innovation relies on leveraging customer insights to develop or improve products that meet evolving customer needs and preferences. Central to this innovation is the ideation process that involves two key stages: identifying customer needs and generating new ideas. While user-generated content offers a rich source of consumer insights, existing approaches for automating the ideation process—including unsupervised learning, supervised learning, deep learning, text summarization and GenAI—face limitations that restrict their scalability and practical utility. Moreover, these approaches often address only isolated stages of the ideation process. Based on a design science methodology and grounded in the user innovation theory, this paper develops and evaluates an integrated GenAI-driven method that automates the ideation process. The method consists of two stages: (1) customer opinion knowledgebase construction and (2) GenAI-based idea generation. The proposed GenAI-driven method offers an adaptable, scalable, and comprehensive solution for advancing customer-driven innovation.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"199 ","pages":"Article 114554"},"PeriodicalIF":6.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145326837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-27DOI: 10.1016/j.dss.2025.114560
Qianqian Wang , Qiang Chen , Sai-Ho Chung , Junmei Rong
Within platform ecosystems, data protection transparency remains insufficient, and research on the dynamic interaction mechanisms governing user data authorization and utilization remains limited. This study develops a stylized analytical model to investigate three interrelated dimensions: platforms' optimal data protection capability (DPC) disclosure strategies, their capacity to enhance user experience, and complementors' utilization levels of user data for product improvement. Key findings are as follows: Platforms voluntarily disclose DPC when their DPC exceeds a critical threshold and disclosure costs are sufficiently low. Platform reputation diminishes disclosure propensity, whereas government reward mechanisms enhance it. Complementors' utilization of reasonably priced user data achieves Pareto improvements by boosting profits for both platforms and complementors. Lower user privacy sensitivity elevates user data authorization ratio, which in turn increases the platform's capability to enhance user experience, and complementors' data utilization levels to improve the product, creating a self-reinforcing cycle of enhanced user utility. While user subsidy and cost-sharing strategies effectively increase user demand and utility, they concurrently reduce platforms' propensity for active DPC disclosure.
{"title":"Data protection capability disclosure strategies and data utilization decisions in platform ecosystems","authors":"Qianqian Wang , Qiang Chen , Sai-Ho Chung , Junmei Rong","doi":"10.1016/j.dss.2025.114560","DOIUrl":"10.1016/j.dss.2025.114560","url":null,"abstract":"<div><div>Within platform ecosystems, data protection transparency remains insufficient, and research on the dynamic interaction mechanisms governing user data authorization and utilization remains limited. This study develops a stylized analytical model to investigate three interrelated dimensions: platforms' optimal data protection capability (DPC) disclosure strategies, their capacity to enhance user experience, and complementors' utilization levels of user data for product improvement. Key findings are as follows: Platforms voluntarily disclose DPC when their DPC exceeds a critical threshold and disclosure costs are sufficiently low. Platform reputation diminishes disclosure propensity, whereas government reward mechanisms enhance it. Complementors' utilization of reasonably priced user data achieves Pareto improvements by boosting profits for both platforms and complementors. Lower user privacy sensitivity elevates user data authorization ratio, which in turn increases the platform's capability to enhance user experience, and complementors' data utilization levels to improve the product, creating a self-reinforcing cycle of enhanced user utility. While user subsidy and cost-sharing strategies effectively increase user demand and utility, they concurrently reduce platforms' propensity for active DPC disclosure.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"199 ","pages":"Article 114560"},"PeriodicalIF":6.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-27DOI: 10.1016/j.dss.2025.114545
Lan Li, Noelle Li Ying Cheah, Seung Hyun Kim
As generative AI continues to transform industries, including the creative sector, it has become critical to understand how it interacts with legal frameworks. This study aims to investigate the effect of the landmark ruling issued by the U.S. District Court on August 18, 2023, which declared AI-generated art uncopyrightable to provide clarity to previously ambiguous legal standards on the AI-related services in online labor markets. Our findings reveal that prices for AI-related gigs on an online freelancer platform dropped by 32.97 % following the ruling, suggesting that the lack of copyright may have reduced the perceived value by limiting clients' residual rights. Furthermore, our research indicates that both freelancer experience and communication efficiency significantly moderate the relationship between AI art non-copyrightability and project pricing. In addition, the results show that large corporate clients were more affected by the ruling than individual clients. In contrast, prices for projects commissioned by small and mid-sized corporate clients did not change significantly. This suggests that large firms are more sensitive to intellectual property uncertainties because they rely heavily on formal rights to secure control and revenue from creative assets. This research contributes to a nuanced understanding of how legal frameworks for AI may shape the gig economy's AI art-related creative services, offering valuable guidelines for more informed decision-making by freelancers, clients, platform owners, and policymakers in this evolving landscape.
{"title":"AI art in the gig economy: Investigating the effects of non-copyrightability in online labor markets","authors":"Lan Li, Noelle Li Ying Cheah, Seung Hyun Kim","doi":"10.1016/j.dss.2025.114545","DOIUrl":"10.1016/j.dss.2025.114545","url":null,"abstract":"<div><div>As generative AI continues to transform industries, including the creative sector, it has become critical to understand how it interacts with legal frameworks. This study aims to investigate the effect of the landmark ruling issued by the U.S. District Court on August 18, 2023, which declared AI-generated art uncopyrightable to provide clarity to previously ambiguous legal standards on the AI-related services in online labor markets. Our findings reveal that prices for AI-related gigs on an online freelancer platform dropped by 32.97 % following the ruling, suggesting that the lack of copyright may have reduced the perceived value by limiting clients' residual rights. Furthermore, our research indicates that both freelancer experience and communication efficiency significantly moderate the relationship between AI art non-copyrightability and project pricing. In addition, the results show that large corporate clients were more affected by the ruling than individual clients. In contrast, prices for projects commissioned by small and mid-sized corporate clients did not change significantly. This suggests that large firms are more sensitive to intellectual property uncertainties because they rely heavily on formal rights to secure control and revenue from creative assets. This research contributes to a nuanced understanding of how legal frameworks for AI may shape the gig economy's AI art-related creative services, offering valuable guidelines for more informed decision-making by freelancers, clients, platform owners, and policymakers in this evolving landscape.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"199 ","pages":"Article 114545"},"PeriodicalIF":6.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-09DOI: 10.1016/j.dss.2025.114555
Wei Du , Qianhui Huang , Ruiyun Xu
Blockchain phishing frauds have caused significant financial losses and eroded trust in blockchain platforms. While existing detection methods increasingly rely on mining transaction networks to identify fraudsters, they often fail to fully exploit transaction patterns or sufficiently model label dependencies—whether between victims and fraudsters or among fraudsters themselves. Informed by criminology theories, we develop a deep learning framework—DeepPhishDetect—that integrates both effective node representation learning and label dependency modeling across transaction networks. DeepPhishDetect models the joint distribution of object labels with a conditional random field (CRF), which can be effectively trained with the variational expectation maximization (EM) framework. Specifically, we design a novel Deep Multi-faceted Detector (DMFD) module to learn complex transactional features in E-step and adopt a Graph Attention Network (GAT) model to profile the label dependencies between fraudsters and victims or among fraudsters in M-step. Experimental results show that DeepPhishDetect significantly outperforms state-of-the-art blockchain phishing detection methods. An ablation study further validates the key design of our model. Intriguingly, a case study demonstrates that our model not only improves accuracy in detecting known phishing accounts but also identifies highly suspicious actors previously overlooked by existing labels. This work contributes to the cybersecurity literature by offering an innovative and more accurate blockchain phishing detection method and enhances business practices in blockchain platform regulation through proactive risk management.
区块链网络钓鱼欺诈造成了重大的经济损失，并侵蚀了对区块链平台的信任。虽然现有的检测方法越来越依赖于挖掘交易网络来识别欺诈者，但它们往往无法充分利用交易模式或充分模拟标签依赖关系——无论是受害者和欺诈者之间还是欺诈者自己之间。根据犯罪学理论，我们开发了一个深度学习框架——deepphishdetect——它集成了有效的节点表示学习和跨交易网络的标签依赖建模。DeepPhishDetect利用条件随机场（conditional random field， CRF）对目标标签的联合分布进行建模，并利用变分期望最大化（variational expectation maximization， EM）框架对目标标签进行有效训练。具体而言，我们设计了一种新颖的深度多面检测器（DMFD）模块来学习e步中的复杂交易特征，并采用图注意网络（GAT）模型来分析m步中欺诈者与受害者之间或欺诈者之间的标签依赖关系。实验结果表明，DeepPhishDetect显著优于最先进的b区块链网络钓鱼检测方法。消融研究进一步验证了我们模型的关键设计。有趣的是，一个案例研究表明，我们的模型不仅提高了检测已知网络钓鱼账户的准确性，而且还识别出了以前被现有标签忽视的高度可疑的参与者。本研究提供了一种创新的、更准确的区块链网络钓鱼检测方法，并通过主动风险管理加强了区块链平台监管的业务实践，为网络安全文献做出了贡献。
{"title":"Follow the vine to get the melon: A deep framework for blockchain phishing fraud detection","authors":"Wei Du , Qianhui Huang , Ruiyun Xu","doi":"10.1016/j.dss.2025.114555","DOIUrl":"10.1016/j.dss.2025.114555","url":null,"abstract":"<div><div>Blockchain phishing frauds have caused significant financial losses and eroded trust in blockchain platforms. While existing detection methods increasingly rely on mining transaction networks to identify fraudsters, they often fail to fully exploit transaction patterns or sufficiently model label dependencies—whether between victims and fraudsters or among fraudsters themselves. Informed by criminology theories, we develop a deep learning framework—DeepPhishDetect—that integrates both effective node representation learning and label dependency modeling across transaction networks. DeepPhishDetect models the joint distribution of object labels with a conditional random field (CRF), which can be effectively trained with the variational expectation maximization (EM) framework. Specifically, we design a novel <em>Deep Multi-faceted Detector (DMFD)</em> module to learn complex transactional features in <em>E</em>-step and adopt a <em>Graph Attention Network (GAT)</em> model to profile the label dependencies between fraudsters and victims or among fraudsters in M-step. Experimental results show that DeepPhishDetect significantly outperforms state-of-the-art blockchain phishing detection methods. An ablation study further validates the key design of our model. Intriguingly, a case study demonstrates that our model not only improves accuracy in detecting known phishing accounts but also identifies highly suspicious actors previously overlooked by existing labels. This work contributes to the cybersecurity literature by offering an innovative and more accurate blockchain phishing detection method and enhances business practices in blockchain platform regulation through proactive risk management.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"199 ","pages":"Article 114555"},"PeriodicalIF":6.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145326835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-27DOI: 10.1016/j.dss.2025.114559
Mi Chang , Eun Hye Jang , Woojin Kim, Daesub Yoon, Do Wook Kang
In Level 3 autonomous driving, drivers must quickly regain manual control when the vehicle exceeds its operational limits. Assessing driver readiness in real-time is crucial, especially under cognitive distraction, as delayed reactions can compromise safety. However, most vehicle systems rely on simple behavioral indicators, such as head movements from visual distractions, and struggle to predict driver readiness under complex cognitive distractions. Moreover, existing studies on cognitive distraction are primarily limited to laboratory settings or surveys, which limits their applicability to real-world driving conditions that require real-time decision making. To address these limitations, this study proposes an in-vehicle decision support system that analyzes cognitive distraction before take-over and predicts driver readiness in real-time. Phase 1 involved experiments with varying levels of cognitive distraction to collect data on driver behavior as well as psychological and physiological states to examine their relationship with driver readiness. Phase 2 used these findings to evaluate and compare deep learning models for predicting driver readiness. The results indicate that driver readiness can be predicted using eye-tracking data, with a model combining a transformer with a Random Forest Regressor achieving the best performance. This study enhances the understanding of the relationship between cognitive distraction and driver readiness. It applies these insights to an in-vehicle decision support system, improving the safety and reliability of autonomous vehicles. Furthermore, it provides a crucial foundation for advancing autonomous system design and driver monitoring technologies.
{"title":"Driver readiness prediction: Bridging cognitive distraction monitoring and in-vehicle decision support systems","authors":"Mi Chang , Eun Hye Jang , Woojin Kim, Daesub Yoon, Do Wook Kang","doi":"10.1016/j.dss.2025.114559","DOIUrl":"10.1016/j.dss.2025.114559","url":null,"abstract":"<div><div>In Level 3 autonomous driving, drivers must quickly regain manual control when the vehicle exceeds its operational limits. Assessing driver readiness in real-time is crucial, especially under cognitive distraction, as delayed reactions can compromise safety. However, most vehicle systems rely on simple behavioral indicators, such as head movements from visual distractions, and struggle to predict driver readiness under complex cognitive distractions. Moreover, existing studies on cognitive distraction are primarily limited to laboratory settings or surveys, which limits their applicability to real-world driving conditions that require real-time decision making. To address these limitations, this study proposes an in-vehicle decision support system that analyzes cognitive distraction before take-over and predicts driver readiness in real-time. Phase 1 involved experiments with varying levels of cognitive distraction to collect data on driver behavior as well as psychological and physiological states to examine their relationship with driver readiness. Phase 2 used these findings to evaluate and compare deep learning models for predicting driver readiness. The results indicate that driver readiness can be predicted using eye-tracking data, with a model combining a transformer with a Random Forest Regressor achieving the best performance. This study enhances the understanding of the relationship between cognitive distraction and driver readiness. It applies these insights to an in-vehicle decision support system, improving the safety and reliability of autonomous vehicles. Furthermore, it provides a crucial foundation for advancing autonomous system design and driver monitoring technologies.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"199 ","pages":"Article 114559"},"PeriodicalIF":6.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-08-28DOI: 10.1016/j.dss.2025.114524
Xuelong Chen , Jinchao Pan
The anonymity and widespread popularity of online social platforms (OSPs) allow users to share uncertain posts freely, leading to numerous rumors. Similar rumors spread widely across OSPs, resulting in frequent cross-platform rumors (CPRs). Owing to the unique nature of the cross-platform spread, the dual challenges of data privacy protection constraints and differences in the data and detection capabilities of OSPs exacerbate the difficulty of CPR detection. Thus, to detect CPRs effectively, we designed and implemented a novel deep learning framework named Cross Platform Rumor Detection based on Improved Federated Learning (CPRDIFL), which integrates and improves federated learning and the pre-trained Masked and Contextualized BERT (MacBERT). Our framework uses FL to analyze data from OSPs independently, thus avoiding the need for data integration and ensuring the data privacy protection of OSPs. Moreover, MacBERT is deployed on the clients of CPRDIFL to extract contextual features from posts and dynamically update local weights based on the data and detection performance. Weight parameters are dynamically shared between clients and servers and between clients to achieve complementary advantages across OSPs. Our framework was used in six comprehensive experiments in different scenarios, and the experimental results showed that it achieved the best results in CPR detection. This study not only provides an effective solution for CPR detection but also marks a significant step toward the automated detection of cross-OSP information pollution.
{"title":"A cross-platform rumor detection framework considering data privacy protection and different detection capabilities of online social platforms","authors":"Xuelong Chen , Jinchao Pan","doi":"10.1016/j.dss.2025.114524","DOIUrl":"10.1016/j.dss.2025.114524","url":null,"abstract":"<div><div>The anonymity and widespread popularity of online social platforms (OSPs) allow users to share uncertain posts freely, leading to numerous rumors. Similar rumors spread widely across OSPs, resulting in frequent cross-platform rumors (CPRs). Owing to the unique nature of the cross-platform spread, the dual challenges of data privacy protection constraints and differences in the data and detection capabilities of OSPs exacerbate the difficulty of CPR detection. Thus, to detect CPRs effectively, we designed and implemented a novel deep learning framework named Cross Platform Rumor Detection based on Improved Federated Learning (CPRDIFL), which integrates and improves federated learning and the pre-trained Masked and Contextualized BERT (MacBERT). Our framework uses FL to analyze data from OSPs independently, thus avoiding the need for data integration and ensuring the data privacy protection of OSPs. Moreover, MacBERT is deployed on the clients of CPRDIFL to extract contextual features from posts and dynamically update local weights based on the data and detection performance. Weight parameters are dynamically shared between clients and servers and between clients to achieve complementary advantages across OSPs. Our framework was used in six comprehensive experiments in different scenarios, and the experimental results showed that it achieved the best results in CPR detection. This study not only provides an effective solution for CPR detection but also marks a significant step toward the automated detection of cross-OSP information pollution.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114524"},"PeriodicalIF":6.8,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-09-08DOI: 10.1016/j.dss.2025.114527
Weiyue Li , Ming Gao , Bowei Chen , Jingmin An , Yeming Gong
Social recommender systems help address data sparsity in user–product interactions by leveraging social relationships to infer user preferences. However, existing models often overlook the role of social capital that influence decision-making in social commerce. Social capital consists of structural, relational, and cognitive dimensions, all of which shape user preferences. To better understand these influences, we propose a multi-task learning framework named DeepSC that integrates social capital theory into preference modeling. Its user preference learning module extracts structural features through graph-based pre-training, learns relational features from dynamic user embeddings, and models cognitive features using a hypergraph attention network. Additionally, the dual graph-based product feature learning module enhances cognitive feature extraction by incorporating product co-interactions. DeepSC is optimized through a joint learning objective, combining point-wise and pair-wise learning with an auxiliary social link prediction task to refine user representations. Experiments on three e-commerce datasets demonstrate that DeepSC significantly outperforms the state-of-the-art recommendation models, highlighting the effectiveness of integrating social capital into social preference learning. Our research advances social recommendation by providing a social capital theory-driven approach to modeling user behavior in digital commerce.
{"title":"Social capital matters: Towards comprehensive user preference for product recommendation with deep learning","authors":"Weiyue Li , Ming Gao , Bowei Chen , Jingmin An , Yeming Gong","doi":"10.1016/j.dss.2025.114527","DOIUrl":"10.1016/j.dss.2025.114527","url":null,"abstract":"<div><div>Social recommender systems help address data sparsity in user–product interactions by leveraging social relationships to infer user preferences. However, existing models often overlook the role of social capital that influence decision-making in social commerce. Social capital consists of structural, relational, and cognitive dimensions, all of which shape user preferences. To better understand these influences, we propose a multi-task learning framework named DeepSC that integrates social capital theory into preference modeling. Its user preference learning module extracts structural features through graph-based pre-training, learns relational features from dynamic user embeddings, and models cognitive features using a hypergraph attention network. Additionally, the dual graph-based product feature learning module enhances cognitive feature extraction by incorporating product co-interactions. DeepSC is optimized through a joint learning objective, combining point-wise and pair-wise learning with an auxiliary social link prediction task to refine user representations. Experiments on three e-commerce datasets demonstrate that DeepSC significantly outperforms the state-of-the-art recommendation models, highlighting the effectiveness of integrating social capital into social preference learning. Our research advances social recommendation by providing a social capital theory-driven approach to modeling user behavior in digital commerce.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114527"},"PeriodicalIF":6.8,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-09-17DOI: 10.1016/j.dss.2025.114542
Yunchang Zhu, Xianghua Lu
Affable design is increasingly employed in AI conversational agents to foster smoother interaction and enhance user experience. However, a growing concern is that this overemphasis on social appeal often overlooks corrective interventions, particularly when users hold false or biased beliefs. Such omissions carry the risk of reinforcing user misconceptions and ultimately undermining the effectiveness of human–AI collaboration. Drawing upon the attribution theory, this study investigates whether the error-correction behavior of AI agents offset these risks and improve user engagement. Empirical evidence from three experimental studies verifies that AI agents' error-correction behavior indeed enhances users' perceived responsibility of AI agents and strengthens their engagement intentions. This effect does not appear to compromise social comfort, especially in the context where responsibility takes precedence, such as healthcare. This study further finds that the high expertise of AI agents amplifies the positive effects of error-correction behavior, while high entitativity diminishes these effects by blurring AI agents' responsibility. These findings offer important guidance for designing responsible AI agents and highlight the value of AI error-correction behaviors in human-AI interaction.
{"title":"Being responsible or affable: Investigating the effects of AI error correction behaviors on user engagement","authors":"Yunchang Zhu, Xianghua Lu","doi":"10.1016/j.dss.2025.114542","DOIUrl":"10.1016/j.dss.2025.114542","url":null,"abstract":"<div><div>Affable design is increasingly employed in AI conversational agents to foster smoother interaction and enhance user experience. However, a growing concern is that this overemphasis on social appeal often overlooks corrective interventions, particularly when users hold false or biased beliefs. Such omissions carry the risk of reinforcing user misconceptions and ultimately undermining the effectiveness of human–AI collaboration. Drawing upon the attribution theory, this study investigates whether the error-correction behavior of AI agents offset these risks and improve user engagement. Empirical evidence from three experimental studies verifies that AI agents' error-correction behavior indeed enhances users' perceived responsibility of AI agents and strengthens their engagement intentions. This effect does not appear to compromise social comfort, especially in the context where responsibility takes precedence, such as healthcare. This study further finds that the high expertise of AI agents amplifies the positive effects of error-correction behavior, while high entitativity diminishes these effects by blurring AI agents' responsibility. These findings offer important guidance for designing responsible AI agents and highlight the value of AI error-correction behaviors in human-AI interaction.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114542"},"PeriodicalIF":6.8,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-09-05DOI: 10.1016/j.dss.2025.114537
An Liu , Xinyu Wang , Jiafu Tang
This paper investigates a trade agent decision optimization problem (TADOP), in which a trade agent (TA) selects a subset of retailers and suppliers to maximize its profit under uncertain demand and spot price. The TA operates between suppliers and retailers as a third-party platform and decide which subset of retailers to serve, taking into account capacity reservations with option suppliers in advance. Once demand and spot price are realized, the TA decides how much to procure from each channel to fulfill retailers' demand. The problem is formulated as a two-stage stochastic program. Due to the high complexity and large number of scenarios, we reformulate the problem as a set-partition model, where the master problem (MP) selects the combination of retailers to serve, and the subproblem (SP) identifies the optimal procurement plans, thus reducing the number of variables and constraints. To further enhance tractability, the SP is transformed into an equivalent shortest-path problem (SPP) to address issues of non-linearity and non-convexity. Experimental results demonstrate the effectiveness of the decomposition approach, providing TAs with a practical decision-making tool for procurement and sales. Furthermore, the insights gained into TAs' procurement and sales strategies across various scenarios offer valuable guidance for decision-making in uncertain supply chain environments.
{"title":"Decision support for integrated trade agent's procurement and sales planning under uncertainty","authors":"An Liu , Xinyu Wang , Jiafu Tang","doi":"10.1016/j.dss.2025.114537","DOIUrl":"10.1016/j.dss.2025.114537","url":null,"abstract":"<div><div>This paper investigates a trade agent decision optimization problem (TADOP), in which a trade agent (TA) selects a subset of retailers and suppliers to maximize its profit under uncertain demand and spot price. The TA operates between suppliers and retailers as a third-party platform and decide which subset of retailers to serve, taking into account capacity reservations with option suppliers in advance. Once demand and spot price are realized, the TA decides how much to procure from each channel to fulfill retailers' demand. The problem is formulated as a two-stage stochastic program. Due to the high complexity and large number of scenarios, we reformulate the problem as a set-partition model, where the master problem (MP) selects the combination of retailers to serve, and the subproblem (SP) identifies the optimal procurement plans, thus reducing the number of variables and constraints. To further enhance tractability, the SP is transformed into an equivalent shortest-path problem (SPP) to address issues of non-linearity and non-convexity. Experimental results demonstrate the effectiveness of the decomposition approach, providing TAs with a practical decision-making tool for procurement and sales. Furthermore, the insights gained into TAs' procurement and sales strategies across various scenarios offer valuable guidance for decision-making in uncertain supply chain environments.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114537"},"PeriodicalIF":6.8,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号