Title: Intelligent precision advertising via deep learning and multi-source data fusion

Authors: Lijing Xu

Addresses: Zhengzhou University of Industrial Technology, Xinzheng 451150, Henan, China

Abstract: With the rapid growth of digital marketing, intelligent advertising recommendation systems play a crucial role in improving delivery efficiency and user experience. Yet, conventional methods often suffer from limited multi-source data fusion, weak interpretability, and poor adaptability across domains. To address these challenges, we propose an intelligent precision advertising framework that integrates heterogeneous data sources using graph neural networks, employs a hybrid deep learning architecture with reinforcement learning for accurate user-ad matching and dynamic optimisation, and incorporates probabilistic modelling to enhance targeting accuracy and cost efficiency. Experiments on real-world datasets show that our approach improves click-through rate by 32.7%, conversion rate by 28.4%, and reduces cost per conversion by 21.5%, while maintaining strong adaptability in dynamic scenarios. These results demonstrate the framework's potential to shift advertising recommendation from static, rule-based delivery to a personalised, interpretable, and real-time paradigm suitable for next-generation marketing systems.

Keywords: precise advertising recommendation; artificial intelligence; multi-source data fusion; dynamic optimisation; deep learning.

DOI: 10.1504/IJBIDM.2026.155252

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.4/5/6, pp.417 - 432

Received: 03 Jun 2025
Accepted: 04 Nov 2025
Published online: 29 Jul 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article