Identifying machine learning techniques for classification of target advertising

Jin A. Choi, Kiho Lim

Research output: Contribution to journalReview articlepeer-review

52 Scopus citations


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.

Original languageEnglish
Pages (from-to)175-180
Number of pages6
JournalICT Express
Issue number3
StatePublished - Sep 2020


  • Artificial intelligence
  • Classification
  • Machine learning
  • Target advertising


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