The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
Identifying machine learning techniques for classification of target advertising
Open accessAbstract
There have been numerous applications of artificial intelligence (AI) technologies to online advertising, especially to optimize the reach of target audiences. Previous studies show that improved computational power significantly advances granular audience targeting capabilities. This study investigates and classifies various machine learning techniques that are used to enhance targeted online advertising. Twenty-three machine learning-based online targeted advertising strategies are identified and classified largely into two categories, user-centric and content-centric approaches. The paper also identifies an underexamined area, algorithm-based detection of click frauds, to illustrate how machine learning approaches can be integrated to preserve the viability of online advertising.
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“Although it is difficult to obtain quantitative data on the impact of AI on e-commerce, the details of which are largely considered sensitive trade secrets, the corresponding machine learning approaches of recommendation and ranking are active topics of research in the AI community, powered by an enormous quantity of user data being continuously gathered through interactions with digital platforms”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [20]
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Cites
Advances in Neural Information Processing Systems 20
Bonilla, E. V., Chai, K. M. A., & Williams, C. K. I. (2008) · neural information processing systems · Cited by 2,701
Simple and Scalable Response Prediction for Display Advertising
Chapelle, O., Manavoglu, E., & Rosales, R. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2532128 · Cited by 392
Real-Time Bidding by Reinforcement Learning in Display Advertising
Cai, H., Ren, K., Zhang, W., Malialis, K., Wang, J., Yu, Y., & Guo, D. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018702 · Cited by 269
Object-Level Video Advertising: An Optimization Framework
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Machine learning for targeted display advertising: transfer learning in action
Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O., & Provost, F. J. (2013) · Machine Learning · doi:10.1007/s10994-013-5375-2 · Cited by 160
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Contextual advertising by combining relevance with click feedback
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Sitcom-star-based clothing retrieval for video advertising: a deep learning framework
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An ensemble learning based approach for impression fraud detection in mobile advertising
Haider, C. M. R., Iqbal, A., Rahman, A. H., & Rahman, M. S. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.02.021 · Cited by 72
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Self-citation
Research works printed but not linked: 1
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Cited by
Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023) · Sage Open · doi:10.1177/21582440231210759 · Cited by 244
Applications of artificial intelligence in B2B marketing: Challenges and future directions
Moradi, M., & Dass, M. (2022) · Industrial Marketing Management · doi:10.1016/j.indmarman.2022.10.016 · Cited by 162
Luccioni, A. S., Strubell, E., & Crawford, K. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732007 · Cited by 71
“…considered sensitive trade secrets, the corresponding machine learning approaches of recommendation and ranking are active topics of research in the AI community, powered by an enormous quantity of user data being continuously gathered through interactions with digital platforms [19, 20, 31].”
Suraña‐Sánchez, C., & Aramendia-Muneta, M. E. (2024) · International Journal of Consumer Studies · doi:10.1111/ijcs.13027 · Cited by 77
Lee, J., Kim, C. G., & Lee, K. C. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102532 · Cited by 60
Metadata
- Authors
- Choi, J.-A., & Lim, K.
- Affiliations
- William Paterson University (US)
- Corresponding author
- Lim, Kiho
- ORCID
- Venue
- ICT Express
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 6
- Issue
- 3
- Pages
- 175-180
- ISSN
- 2405-9595 (ISSN-L)
- OpenAlex
- W3033252662
- Cited by
- 139
- References
- 31
- Citations by year
- 2026: 15 · 2025: 27 · 2024: 43 · 2023: 25 · 2022: 19 · 2021: 8 · 2020: 2
- FWCI
- 12.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- ICT Express (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- William Paterson University of New Jersey
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- machine learning methods, audience targeting, user-centric approach, click fraud detection
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