A Foundational Framework for Intelligent Data-Driven Decision Support Systems Based on Adaptive Preference Learning
Keywords:
Intelligent Decision Support Systems, Adaptive Preference Learning, Data-Driven Decision Making, Artificial Intelligence, Decision IntelligenceAbstract
The increasing complexity of organizational decision-making, driven by heterogeneous data, evolving user preferences, and dynamic business environments, has exposed the limitations of conventional Decision Support Systems (DSS). Traditional DSS rely on static decision models and predefined preferences, limiting their adaptability and personalization. Although Artificial Intelligence (AI)-based DSS have improved predictive capabilities, many still lack adaptive preference learning, continuous feedback, explainability, and lifelong learning. This study aims to develop a Foundational Framework for Intelligent Data-Driven Decision Support Systems (ID-DSS) based on Adaptive Preference Learning (APL). The research adopts the Design Science Research (DSR) methodology, incorporating a systematic literature review, problem identification, requirement analysis, framework design, and conceptual validation. The proposed framework integrates data analytics, adaptive preference learning, decision intelligence, explainable AI, continuous feedback, and knowledge updating within a closed-loop learning architecture. The Adaptive Preference Learning mechanism continuously refines user preferences using explicit feedback, implicit behavioral observations, contextual information, and incremental learning, enabling recommendations to become increasingly personalized and adaptive. Furthermore, explainable AI enhances transparency by providing interpretable reasoning for recommendation outcomes. The proposed framework establishes a theoretical foundation for next-generation intelligent DSS that are adaptive, personalized, transparent, context-aware, and capable of continuous learning, with potential applications across healthcare, finance, manufacturing, education, smart cities, and public administration.
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P. Mikalef, M. Boura, G. Lekakos, and J. Krogstie, “Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and moderating effect of the environment,” Br. J. Manag., vol. 30, no. 2, pp. 272–298, 2019.
T. R. Rao, P. Mitra, R. Bhatt, and A. Goswami, “The big data system, components, tools, and technologies: A survey.,” Knowl. Inf. Syst., vol. 60, no. 3, p. 1165, 2019.
N. Zahariadis, Ambiguity and choice in public policy: Political decision making in modern democracies. Georgetown university press, 2003.
J. Taylor and N. Raden, Smart Enough Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions. Pearson Education, 2007.
M. M. Afsar, T. Crump, and B. Far, “Reinforcement learning based recommender systems: A survey,” ACM Comput. Surv., vol. 55, no. 7, pp. 1–38, 2022.
S. R. BasiReddy, “From static personalization to adaptive intelligence: Building context-aware CRM recommendation systems with AI agents,” Int. J. Sci. Eng. Technol., vol. 10, no. 3, 2022.
S. Gregor and A. R. Hevner, “Positioning and presenting design science research for maximum impact1,” MIS Q., vol. 37, no. 2, pp. 337–355, 2013.
R. H. Tsiotsou, B. Koles, J. Paul, and S. M. C. Loureiro, “Theory generation from literature reviews: A methodological guidance,” Int. J. Consum. Stud., vol. 46, no. 5, pp. 1505–1516, 2022.
G. D. Bhatt and J. Zaveri, “The enabling role of decision support systems in organizational learning,” Decis. Support Syst., vol. 32, no. 3, pp. 297–309, 2002.
K. Thórisson and H. Helgasson, “Cognitive architectures and autonomy: A comparative review,” J. Artif. Gen. Intell., vol. 3, no. 2, pp. 1–30, 2012.
M. Doumpos and E. Grigoroudis, Multicriteria decision aid and artificial intelligence: links, theory and applications. John Wiley & Sons, 2013.
Q. N. Nguyen and F. Ricci, “User preferences initialization and integration in critique-based mobile recommender systems,” in Proceedings of Workshop on Artificial Intelligence in Mobile Systems, 2004.
Z. Lu, X. Chen, Q. Li, X. Zhang, and P. Zhou, “A hand gesture recognition framework and wearable gesture-based interaction prototype for mobile devices,” IEEE Trans. human-machine Syst., vol. 44, no. 2, pp. 293–299, 2014.
B. O. Eboseremen et al., “Developing an AI-driven personalization pipeline for customer retention in investment platforms,” J. Front. Multidiscip. Res., vol. 3, no. 1, pp. 593–606, 2022.
P. Verma, S. K. Sood, and S. Kalra, “Cloud-centric IoT based student healthcare monitoring framework,” J. Ambient Intell. Humaniz. Comput., vol. 9, no. 5, pp. 1293–1309, 2018.
D. Akanbi and S. R. Sales, “Building automated decision engines that merge operational intelligence with workflow robotics to significantly elevate enterprise throughput, accuracy, and performance stability,” Int. J. Comput. Appl. Technol. Res., vol. 9, no. 12, pp. 487–499, 2020.
E. Frias-Martinez, S. Y. Chen, and X. Liu, “Survey of data mining approaches to user modeling for adaptive hypermedia,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 36, no. 6, pp. 734–749, 2006.
A. Akanbi and M. Masinde, “A distributed stream processing middleware framework for real-time analysis of heterogeneous data on big data platform: Case of environmental monitoring,” Sensors, vol. 20, no. 11, p. 3166, 2020.
C. Limongelli, F. Sciarrone, M. Temperini, and G. Vaste, “Adaptive learning with the LS-plan system: a field evaluation,” IEEE Trans. Learn. Technol., vol. 2, no. 3, pp. 203–215, 2009.
Y. Zhang and X. Chen, “Explainable recommendation: A survey and new perspectives,” Found. Trends® Inf. Retr., vol. 14, no. 1, pp. 1–101, 2020.
P. Ghavami, Big data analytics methods: analytics techniques in data mining, deep learning and natural language processing. Walter de Gruyter GmbH & Co KG, 2019.
J. Lee, H.-A. Kao, and S. Yang, “Service innovation and smart analytics for industry 4.0 and big data environment,” Procedia cirp, vol. 16, pp. 3–8, 2014.
S. Gupta, S. Modgil, S. Bhattacharyya, and I. Bose, “Artificial intelligence for decision support systems in the field of operations research: review and future scope of research,” Ann. Oper. Res., vol. 308, no. 1, pp. 215–274, 2022.
A. Nimalasena, “Self-adaptation via concurrent multi-action evaluation for unknown context.” University of Westminster, 2017.
R. Kazhamiakin, S. Benbernou, L. Baresi, P. Plebani, M. Uhlig, and O. Barais, “Adaptation of service-based systems,” in Service Research Challenges and Solutions for the Future Internet: S-Cube–Towards Engineering, Managing and Adapting Service-Based Systems, Springer, 2010, pp. 117–156.
S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A survey and new perspectives,” ACM Comput. Surv., vol. 52, no. 1, pp. 1–38, 2019.
J. Fürnkranz, E. Hüllermeier, W. Cheng, and S.-H. Park, “Preference-based reinforcement learning: a formal framework and a policy iteration algorithm,” Mach. Learn., vol. 89, no. 1, pp. 123–156, 2012.
A. Das and P. Rad, “Opportunities and challenges in explainable artificial intelligence (xai): A survey,” arXiv Prepr. arXiv2006.11371, 2020.
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