论文标题
赞助商搜索广告中的关键字优化：多级计算框架
Keyword Optimization in Sponsored Search Advertising: A Multi-Level Computational Framework
论文作者
论文摘要
在赞助的搜索广告中，关键字是链接广告客户，搜索用户和搜索引擎的必不可少的桥梁。广告客户必须在整个搜索广告活动的整个生命周期中处理一系列关键字决策。本文提出了一个多级和封闭形式的计算框架，用于关键字优化（MKOF），以支持各种关键字决策。基于此框架，我们为关键字定位，关键字分配和关键字分组的相应优化策略（例如，市场，广告系列和ADGroup）。通过从过去的搜索广告活动中获得的两个现实世界数据集，我们进行了计算实验，以评估我们的关键字优化框架和实例化策略。实验结果表明，我们的方法可以以稳定的方式达到最佳解决方案，并且优于实践中常用的两个基线关键字策略。拟议的MKOF框架还提供了一个有效的实验环境，可以在赞助搜索广告中实施和评估各种关键字策略。
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.
