Keyword Optimization in Sponsored Search Advertising: A Multi-Level Computational Framework

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

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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 multi-level 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
JournalIEEE Intelligent Systems
DOIs
Publication statusAccepted/In press - 1 Jan 2019
Externally publishedYes

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Keywords

  • Advertising
  • advertising strategy
  • Economics
  • Intelligent systems
  • Investment
  • keyword optimization
  • keyword strategy
  • Management information systems
  • Optimization
  • search advertising
  • Search engines

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

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