Decision Support Systems最新文献

{"title":"Knowledge-based Context-aware Group Recommender System for Point of Interest recommendation","authors":"Nargis Pervin ,&nbsp;Abhishek Kulkarni ,&nbsp;Ayush Adarsh ,&nbsp;Shreya Som","doi":"10.1016/j.dss.2025.114485","DOIUrl":"10.1016/j.dss.2025.114485","url":null,"abstract":"<div><div>The rise of Location-based Social Networking (LBSN) platforms has transformed the way users explore Points of Interest (POIs), increasingly relying on group-based recommendations. However, recommending POIs to groups presents unique challenges due to conflicting preferences among members. Traditional group recommendation algorithms often prioritize aggregated methods or explicit preference extraction, overlooking latent domain-specific information and the dynamic nature of group decision-making. To address these gaps, we propose a novel Knowledge-based Context-Aware Group Recommender System (KCGRS) designed to support decision-making processes within groups. KCGRS operates in two key stages: first, it utilizes a knowledge graph to learn domain-specific embeddings for both users and POIs, ensuring that implicit preferences and contextual factors are incorporated. In the second stage, these embeddings are enhanced with contextual information using a feed-forward transformer model, allowing for a more nuanced understanding of real-time preferences. The decision-making process is further refined by generating a group embedding, which is computed by applying a weighted aggregate of the context-infused embeddings of individual group members. This approach models group dynamics and decision processes more accurately, ensuring that the final recommendation reflects the collective preferences of the group. Experiments on real-world Yelp data show that KCGRS significantly outperforms five state-of-the-art baselines, delivering up to an average of 14.15% improvement in Hit ratio and a 13.07% increase in NDCG compared to the next best method while also maintaining competitive runtime efficiency. Furthermore, KCGRS demonstrates enhanced diversity and coverage in recommendations, ensuring that POI suggestions cater to a broader range of user preferences while avoiding over-personalization. This balance between accuracy, diversity, and efficiency highlights KCGRS’s effectiveness in supporting group decision-making and its potential to enhance collaborative recommendations in LBSN platforms. Finally, a user study with 144 participants was conducted that resulted in statistically significant levels of user satisfaction and trust in the recommendations, thereby supporting the practical effectiveness of the KCGRS system.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114485"},"PeriodicalIF":6.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322516","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}

{"title":"An explainable lesion detection transformer model for medical imaging diagnosis decision support: Design science research","authors":"Xinwei Wang ,&nbsp;Yi Feng ,&nbsp;Sutong Wang ,&nbsp;Dujuan Wang ,&nbsp;T.C.E. Cheng","doi":"10.1016/j.dss.2025.114492","DOIUrl":"10.1016/j.dss.2025.114492","url":null,"abstract":"<div><div>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 <span><span>https://github.com/weimingai/EL-DETR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114492"},"PeriodicalIF":6.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291619","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}

{"title":"Investigating the impact of differential privacy obfuscation on users’ data disclosure decisions","authors":"Michael Khavkin,&nbsp;Eran Toch","doi":"10.1016/j.dss.2025.114474","DOIUrl":"10.1016/j.dss.2025.114474","url":null,"abstract":"<div><div>Differential Privacy (DP) has emerged as the standard for privacy-preserving analysis of individual-level data. Despite growing attention in the research community to the operationalization of DP through the selection of the privacy budget <span><math><mi>ɛ</mi></math></span>, little is known about how DP obfuscation affects users’ disclosure decisions in data market scenarios. These decisions may be context-specific and vary with privacy preferences, eliciting disparate data valuations across individuals. Through a choice-based conjoint analysis (<span><math><mrow><msub><mrow><mi>N</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><mn>588</mn></mrow></math></span>), simulating realistic data markets, we analyzed how varying DP protection levels influence individual decision-making of participation in data collection under DP. Our findings show that personal reward and the guaranteed DP protection had the strongest influence on participants’ selection of a data collection scenario. Surprisingly, the type of disclosed data had the least influence on participants’ decisions to disclose personal data, a trend consistent across participants from different countries. Furthermore, increasing the DP protection level by a single unit reduced the preferred compensation price by over 60% for the same level of user utility, with marginal effects diminishing exponentially at higher DP levels. Our results were then confirmed in an online study (<span><math><mrow><msub><mrow><mi>N</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><mn>146</mn></mrow></math></span>) involving real data disclosure with actual payments, using our original scenario framing. Our findings can support context-specific DP configuration and help data practitioners improve decision-making associated with privacy protection in differentially private systems, balancing the trade-off between DP and compensation costs.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114474"},"PeriodicalIF":6.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254153","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}

{"title":"Social media meets FinTech platforms: How do online emotions support credit risk decision-making?","authors":"Zenan Zhou ,&nbsp;Zhichen Chen ,&nbsp;Yingjie Zhang ,&nbsp;Tian Lu ,&nbsp;Xianghua Lu","doi":"10.1016/j.dss.2025.114471","DOIUrl":"10.1016/j.dss.2025.114471","url":null,"abstract":"<div><div>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 “<em>a bright side of anger</em>” 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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"195 ","pages":"Article 114471"},"PeriodicalIF":6.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167967","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}

{"title":"Multi-interest transfer using contrastive learning for cross-domain recommendation","authors":"Yu-Lin Lai ,&nbsp;Szu-Hao Huang ,&nbsp;Chiao-Ting Chen ,&nbsp;Cheng-Jhang Wu","doi":"10.1016/j.dss.2025.114473","DOIUrl":"10.1016/j.dss.2025.114473","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"195 ","pages":"Article 114473"},"PeriodicalIF":6.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185389","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}

{"title":"More than meets the eye: Feature concerns and suggestions in mobile XR app reviews","authors":"Nohel Zaman ,&nbsp;David M. Goldberg ,&nbsp;Zhilei Qiao ,&nbsp;Alan S. Abrahams","doi":"10.1016/j.dss.2025.114475","DOIUrl":"10.1016/j.dss.2025.114475","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"195 ","pages":"Article 114475"},"PeriodicalIF":6.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098457","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}

{"title":"Integrating temporal association rules into intelligent prediction system for metabolic dysfunction-associated fatty liver disease","authors":"Zhuoqing Wu ,&nbsp;Chonghui Guo ,&nbsp;Jingfeng Chen ,&nbsp;Suying Ding ,&nbsp;Yunchao Zheng","doi":"10.1016/j.dss.2025.114467","DOIUrl":"10.1016/j.dss.2025.114467","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"195 ","pages":"Article 114467"},"PeriodicalIF":6.7,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084474","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}

{"title":"40 years of Decision Support Systems: A bibliometric analysis","authors":"Li Guan,&nbsp;José M. Merigó,&nbsp;Ghassan Beydoun","doi":"10.1016/j.dss.2025.114469","DOIUrl":"10.1016/j.dss.2025.114469","url":null,"abstract":"<div><div><em>Decision Support Systems</em> (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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"194 ","pages":"Article 114469"},"PeriodicalIF":6.7,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943493","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}

{"title":"Unlocking big data success in the AI-driven era: Toward a unified theory for intelligent decision support","authors":"Xi Zhao ,&nbsp;Hua Dai ,&nbsp;Tao “Eric” Hu ,&nbsp;Hsing K. Cheng ,&nbsp;Ping Zhang","doi":"10.1016/j.dss.2025.114468","DOIUrl":"10.1016/j.dss.2025.114468","url":null,"abstract":"<div><div>Upon a grounded theory-based literature review of 220 articles published in the AIS “Senior Scholars' Basket of Journals” over the period of twenty years of 2000–2020, this study examines concepts, constructs, topics, methodologies, and research models/paradigms of Big Data literature in the information systems (IS) discipline. We extend the well-established IS success model into the Big Data area, synthesize theoretical perspectives and empirical findings of literature, identify critical success factors and interrelationships, and develop a unified Big Data success theory in the organizational context. Building upon the literature review, we propose a set of research agendas and articulate opportunities and challenges of the evolving Big Data literature. The paper concludes with research implications, contributions, and limitations of the study in the ever-emerging AI-Driven era.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"194 ","pages":"Article 114468"},"PeriodicalIF":6.7,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943492","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}

{"title":"Product return prediction in live streaming e-commerce with cross-modal contrastive transformer","authors":"Wen Zhang ,&nbsp;Rui Xie ,&nbsp;Pei Quan ,&nbsp;Zhenzhong Ma","doi":"10.1016/j.dss.2025.114470","DOIUrl":"10.1016/j.dss.2025.114470","url":null,"abstract":"<div><div>The live-streaming e-commerce industry is suffering heavy economic losses due to the high product return rate, which leads to rising logistics costs, greater inventory pressure, and unsatisfactory consumer experiences. Accurate product return prediction is highly desirable for the vendors to optimize their business operations in advance to reduce return-related costs. This paper proposes a novel approach, called Contraformer (Contrastive transformer), to predict product returns in live streaming e-commerce by leveraging fine-grained streamer behavior features extracted from three modalities (i.e., visual, acoustic, and language). The primary contribution lies in that we adopt Transformer with the encoder-decoder architecture with a novel class-supervised contrastive learning (CSCL) to fuse streamer behavior for multimodal representation alignment and inter-modal interaction characterization. By using a real-world dataset with 2584 product streamers and 864 items collected from Tiktok China live streaming platform, we demonstrate that the proposed Contrasformer approach outperforms the baseline methods in predicting product return rate with a 25 % reduction in terms of mean absolute error. This study offers great managerial implications for vendors to manage their practice in live streaming commerce.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"194 ","pages":"Article 114470"},"PeriodicalIF":6.7,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922198","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}