Journal: Decision Support Systems

Abbreviation

Decis. support syst.

Publisher

Elsevier

Journal Volumes

ISSN

0167-9236
1873-5797

Description

Filters

2011 - 2019122020 - 20252
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Publications 1 - 10 of 14
  • Manipulative imputations in a distributed decision support setting: The effects of information asymmetry and aggregation complexity
    Item type: Journal Article
    Malekovic, Ninoslav; Sutanto, Juliana; Goutas, Lazaros (2016)
    Decision Support Systems
  • Long-term stock index forecasting based on text mining of regulatory disclosures
    Item type: Journal Article
    Feuerriegel, Stefan; Gordon, Julius (2018)
    Decision Support Systems
  • Deep learning for affective computing: Text-based emotion recognition in decision support
    Item type: Journal Article
    Kratzwald, Bernhard; Ilić, Suzana; Kraus, Mathias; et al. (2018)
    Decision Support Systems
  • Uncovering the relationship between OSS user support networks and OSS popularity
    Item type: Journal Article
    Sutanto, Juliana; Kankanhalli, Atreyi; Tan, Bernard C.Y. (2014)
    Decision Support Systems
  • Decision support from financial disclosures with deep neural networks and transfer learning
    Item type: Journal Article
    Kraus, Mathias; Feuerriegel, Stefan (2017)
    Decision Support Systems
  • RFID-enabled shelf replenishment with backroom monitoring in retail stores
    Item type: Journal Article
    Condea, Cosmin; Thiesse, Frédéric; Fleisch, Elgar (2012)
    Decision Support Systems
  • Emotion aware session based news recommender systems
    Item type: Journal Article
    Gundersen, Benjamin; Kalloori, Saikishore; Srivastava, Abhishek (2025)
    Decision Support Systems
    News recommender systems are decision support systems that exploit user-article interactions over a short duration of time to discover users’ interests and predict unseen news articles to generate a ranking of news articles that are relevant and interesting. In the news recommendation scenario, the relevance of articles decays quickly, and fresh articles are generated daily. Session based models are proposed using time-aware approaches to exploit interactions sequentially. Prior news recommender systems do not consider emotional information expressed in news articles within sessions for recommendations. Emotions play a key role in supporting decision-making and emotionally charged headlines can evoke curiosity or urgency, prompting users to click on certain articles. This paper presents an innovative decision support system for session based news recommendation, using expressed emotions from news articles, such as expressed in the title, abstract, and text, to improve user decision-making. We introduce a novel methodology that incorporates expressed emotions into three session based news recommendation models. Our results demonstrate that expressed emotion carries valuable information to improve session based news recommenders on various ranking metrics significantly and proved especially beneficial in scenarios with limited user interaction history, addressing the cold-start problem. The results show significant improvements in ranking metrics, emphasizing the utility of emotional features for dynamic decision-making support.
  • Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences
    Item type: Journal Article
    Kraus, Mathias; Feuerriegel, Stefan (2019)
    Decision Support Systems
    Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the (conditional) expected remaining useful life as a prognostic. This approach finds widespread use in practice because of its high explanatory power. A more accurate alternative is promised by machine learning, where forecasts incorporate deterioration processes and environmental variables through sensor data. However, machine learning largely functions as a black-box method and its forecasts thus forfeit most of the desired interpretability. As our primary contribution, we propose a structured-effect neural network for predicting the remaining useful life which combines the favorable properties of both approaches: its key innovation is that it offers both a high accountability and the flexibility of deep learning. The parameters are estimated via variational Bayesian inferences. The different approaches are compared based on the actual time-to-failure for aircraft engines. This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.
  • Evaluation and aggregation of pay-as-you-drive insurance rate factors
    Item type: Journal Article
    Paefgen, Johannes; Staake, Thorsten; Thiesse, Frédéric (2014)
    Decision Support Systems
  • Preventing traffic accidents with in-vehicle decision support systems - The impact of accident hotspot warnings on driver behaviour
    Item type: Journal Article
    Ryder, Benjamin; Gahr, Bernhard; Egolf, Philipp; et al. (2017)
    Decision Support Systems
Publications 1 - 10 of 14