Artificial Intelligence for Personalized Digital Advertising: Methods and Applications
DOI:
https://doi.org/10.66280/ijair.v1i1.52Keywords:
personalized digital advertising; artificial intelligence; recommender systems; real-time bidding; privacy; fairness; governance; socio-technical systems; causal inference; generative AIAbstract
Artificial intelligence has become the central computational substrate of contemporary personalized digital advertising. What was once a relatively discrete function of audience segmentation and campaign optimization has evolved into a densely interconnected socio-technical system spanning large-scale data infrastructures, real-time bidding markets, recommender architectures, creative generation systems, attribution pipelines, privacy controls, platform governance mechanisms, and emerging regulatory regimes. This paper presents a system-level examination of artificial intelligence for personalized digital advertising, with emphasis on methods, architectures, applications, and structural trade-offs. Rather than treating personalization solely as a prediction problem, the paper situates AI-enabled advertising within a broader infrastructure in which model performance, data governance, fairness, robustness, and sustainability are co-produced by technical design and institutional arrangements. The discussion integrates methods from machine learning, recommender systems, natural language processing, computer vision, causal inference, reinforcement learning, privacy-preserving computation, and algorithmic governance. It analyzes how these methods are deployed across demand-side platforms, ad exchanges, publishers, retail media networks, social media ecosystems, and omnichannel measurement environments. Particular attention is given to tensions between relevance and manipulation, efficiency and opacity, personalization and privacy, automation and accountability, as well as innovation and regulatory compliance. The paper argues that the long-term viability of AI in digital advertising depends not merely on improved predictive accuracy but on the development of resilient, interpretable, policy-aware, and socially legitimate infrastructures. It concludes by outlining future research directions centered on trustworthy personalization, multimodal generative systems, causal measurement, sustainable computation, and governance frameworks capable of aligning commercial objectives with public values.
References
1.Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.
2.Athey, S., & Imbens, G. W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2), 3–32.
3.Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.
4.Bottou, L., Peters, J., Quinonero-Candela, J., Charles, D. X., Chickering, D. M., Portugaly, E., Ray, D., Simard, P., & Snelson, E. (2013). Counterfactual reasoning and learning systems: The example of computational advertising. Journal of Machine Learning Research, 14, 3207–3260.
5.Broder, A. (2008). Computational advertising and recommender systems. In Proceedings of the 2008 ACM Conference on Recommender Systems (pp. 1–2). ACM.
6.Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., Anil, R., Haque, Z., Hong, L., Jain, V., Liu, X., Shah, H., & Chi, E. H. (2016). Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (pp. 7–10). ACM.
7.Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for YouTube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 191–198). ACM.
8.Dwork, C. (2008). Differential privacy: A survey of results. In Proceedings of the 5th International Conference on Theory and Applications of Models of Computation (pp. 1–19). Springer.
9.Edelman, B., Ostrovsky, M., & Schwarz, M. (2007). Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American Economic Review, 97(1), 242–259.
10.Goldfarb, A., & Tucker, C. (2011). Privacy regulation and online advertising. Management Science, 57(1), 57–71.
11.Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017). DeepFM: A factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 1725–1731). IJCAI.
12.He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (pp. 173–182). International World Wide Web Conferences Steering Committee.
13.Jannach, D., Lerche, L., Kamehkhosh, I., & Jugovac, M. (2017). What recommenders recommend: An analysis of recommendation biases and possible countermeasures. User Modeling and User-Adapted Interaction, 27(5), 427–491.
14.Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.
15.Lambrecht, A., & Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2966–2981.
16.Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010). A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th International Conference on World Wide Web (pp. 661–670). ACM.
17.Liu, Q., Zeng, Y., Mokhosi, R., & Zhang, H. (2020). STAMP: Short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1831–1839). ACM.
18.McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (pp. 1273–1282). PMLR.
19.Nissenbaum, H. (2010). Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press.
20.O’Donovan, J., Gain, J., & Marais, P. (2005). A case study in the personalization of music recommendation. User Modeling and User-Adapted Interaction, 15(3–4), 355–382.
21.Rendle, S. (2010). Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995–1000). IEEE.
22.Richardson, M., Dominowska, E., & Ragno, R. (2007). Predicting clicks: Estimating the click-through rate for new ads. In Proceedings of the 16th International Conference on World Wide Web (pp. 521–530). ACM.
23.Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender systems handbook (pp. 257–297). Springer.
24.Wang, Y. (2025, June). RAGNet: Transformer–GNN–Enhanced Cox–Logistic Hybrid Model for Rheumatoid Arthritis Risk Prediction. In Proceedings of the 2025 International Conference on Health Informatization and Data Analytics (pp. 90-94).
25.Yi, X. (2025, October). Real-Time Fair-Exposure Ad Allocation for SMBs and Underserved Creators via Contextual Bandits-with-Knapsacks. In Proceedings of the 2025 2nd International Conference on Digital Economy and Computer Science (pp. 1602-1607).
26.Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020, February). InP grating coupler design for vertical coupling of InP and silicon chips. In Integrated Optics: Devices, Materials, and Technologies XXIV (Vol. 11283, pp. 33-38). SPIE.
27.Li, B. (2025). GIS-Integrated Semi-Supervised U-Net for Automated Spatiotemporal Detection and Visualization of Land Encroachment in Protected Areas Using Remote Sensing Imagery.
28.Chang, C., Fu, M., Chen, X., Feng, S., Zhang, M., Zhou, X., ... & Liu, Z. (2025, November). Research on PDU-Net Lung Nodule Segmentation Algorithm Based on Path Aggregation and Dual Attention. In 2025 4th International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) (pp. 1897-1900). IEEE.
29.Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020). Design and Optimization of Shallow-Angle Grating Coupler for Vertical Emission from Indium Phosphide Devices.
30.HOU, R., JEONG, S., WANG, Y., LAW, K. H., & LYNCH, J. P. (2017). Camera-based triggering of bridge structural health monitoring systems using a cyber-physical system framework. Structural Health Monitoring 2017, (shm).
31.Qi, R. (2025). AUBIQ: A Generative AI-Powered Framework for Automating Business Intelligence Requirements in Resource-Constrained Enterprises. Frontiers in Business and Finance, 2(01), 66-86.
32.Qi, R. (2025, June). Enterprise financial distress prediction based on machine learning and SHAP interpretability analysis. In Proceedings of the 2025 International Conference on Artificial Intelligence and Digital Finance (pp. 76-79).
33.Qi, R. (2025, July). DecisionFlow for SMEs: A Lightweight Visual Framework for Multi-Task Joint Prediction and Anomaly Detection. In Proceedings of the 2025 International Conference on Economic Management and Big Data Application (pp. 899-903).
34.Yang, D. (2022). An Investigation on English Translations of Culture-Loaded Words in The Analects of Confucius from the Eco Perspective: A Case Study of the English Translation of Lectures on China’s Traditional Political Thoughts. Editorial Board, 7.
35.Dan, Y. A. N. G. AN ANALYSIS OF THE IN-DEPTH TRANSLATION STRATEGY OF THE ENGLISH EDITION OF LECTURES ON CHINA’S TRADITIONAL POLITICAL THOUGHTS.
36.YANG, D., & WANG, Z. A Study on Evaluation of the Integration of Chinese and Foreign Cultures into Oxford Junior High School English Textbooks on the Basis of Multicultural Education. Editorial Board, 33.
37.Tian, Y., Xu, S., Cao, Y., Wang, Z., & Wei, Z. (2025). An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection. Mathematics, 13(13), 2086.
38.Li, B. (2025). GIS-Integrated Semi-Supervised U-Net for Automated Spatiotemporal Detection and Visualization of Land Encroachment in Protected Areas Using Remote Sensing Imagery.
39.Zhang, T. (2025). A Neuro-Symbolic and Blockchain-Enhanced Multi-Agent Framework for Fair and Consistent Cross-Regulatory Audit Intelligence.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
