Keyword Optimization in Sponsored Search Advertising: A Multilevel Computational Framework

Yanwu Yang, Bernard J. Jansen, Yinghui Yang, Xunhua Guo, Daniel Zeng

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


In sponsored search advertising, keywords serve as an essential bridge linking advertisers, search users, and search engines. Advertisers have to deal with a series of keyword decisions throughout the entire lifecycle of search advertising campaigns. This paper proposes a multilevel and closed-form computational framework for keyword optimization (MKOF) to support various keyword decisions. Based on this framework, we develop corresponding optimization strategies for keyword targeting, keyword assignment, and keyword grouping at different levels (e.g., market, campaign, and adgroup). With two real-world datasets obtained from past search advertising campaigns, we conduct computational experiments to evaluate our keyword optimization framework and instantiated strategies. Experimental results show that our method can approach the optimal solution in a steady way, and it outperforms two baseline keyword strategies commonly used in practice. The proposed MKOF framework also provides a valid experimental environment to implement and assess various keyword strategies in sponsored search advertising.

Original languageEnglish (US)
Article number8613840
Pages (from-to)32-42
Number of pages11
JournalIEEE Intelligent Systems
Issue number1
StatePublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Artificial Intelligence


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