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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”
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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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