Download pdf
Applications of stochastic processes and reinforcement learning in strategic decision support and personalized ad recommendation: An AIGC study
- 1 Beijing University of Posts and Telecommunications, Beijing, China
* Author to whom correspondence should be addressed.
Abstract
This paper investigates the integration of stochastic processes and reinforcement learning (RL) in strategic decision support systems (SDSS) and personalized advertisement recommendations. Stochastic processes offer a robust framework for modeling uncertainties and predicting future states across various domains, while RL facilitates dynamic optimization through continuous interaction with the environment. The combination of these technologies significantly enhances decision-making accuracy and efficiency, yielding substantial benefits in industries such as financial services, healthcare, logistics, retail, and manufacturing. By leveraging these advanced AI techniques, businesses can develop adaptive strategies that respond to real-time changes and optimize outcomes. This paper delves into the theoretical foundations of stochastic processes and RL, explores their practical implementations, and presents case studies that demonstrate their effectiveness. Furthermore, it addresses the computational complexity and ethical considerations related to these technologies, providing comprehensive insights into their potential and challenges. The findings highlight the transformative impact of integrating stochastic processes and RL in contemporary decision-making frameworks.
Keywords
Stochastic processes, reinforcement learning, strategic decision support systems, personalized advertisement recommendations
[1]. Wang, Fuzhang, et al. “Artificial intelligence and stochastic optimization algorithms for the chaotic datasets.” FRACTALS (fractals) 31.06 (2023): 1-14.
[2]. Albergo, Michael S., Nicholas M. Boffi, and Eric Vanden-Eijnden. “Stochastic interpolants: A unifying framework for flows and diffusions.” arXiv preprint arXiv:2303.08797 (2023).
[3]. Faulwasser, Timm, et al. “Behavioral theory for stochastic systems? A data-driven journey from Willems to Wiener and back again.” Annual Reviews in Control (2023).
[4]. Dutordoir, Vincent, et al. “Neural diffusion processes.” International Conference on Machine Learning. PMLR, 2023.
[5]. Rosati, Riccardo, et al. “From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0.” Journal of Intelligent Manufacturing 34.1 (2023): 107-121.
[6]. Higgins, Oliver, et al. “Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review.” International Journal of Mental Health Nursing 32.4 (2023): 966-978.
[7]. Ali, Md Ramjan, Shah Md Ashiquzzaman Nipu, and Sharfuddin Ahmed Khan. “A decision support system for classifying supplier selection criteria using machine learning and random forest approach.” Decision Analytics Journal 7 (2023): 100238.
[8]. Valluri, Durga Deepak. “Exploring cognitive reflection for decision-making in robots: Insights and implications.” International Journal of Science and Research Archive 11.2 (2024): 518-530.
[9]. Rolf, Benjamin, et al. “A review on reinforcement learning algorithms and applications in supply chain management.” International Journal of Production Research 61.20 (2023): 7151-7179.
[10]. Hasan, MD Rokibul. “Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective.” Journal of Business and Management Studies 6.2 (2024): 100-109.
Cite this article
Ma,Q. (2024). Applications of stochastic processes and reinforcement learning in strategic decision support and personalized ad recommendation: An AIGC study. Applied and Computational Engineering,87,66-71.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 6th International Conference on Computing and Data Science
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).