A Hierarchical Bayesian Network-Based Approach to Keyword Auction
A Hierarchical Bayesian Network-Based Approach to Keyword Auction
复制标题
DOI：
10.1109/tem.2015.2390772
复制
发表时间：
2015-02
影响因子：
5.8
通讯作者：
Liwen Hou
中科院分区：
文献类型：
--
作者：
Liwen Hou
网络关键词拍卖的繁荣极大地促进了搜索引擎营销在各行业的渗透。然而，目前搜索引擎的运行规则使得那些缺乏经验的广告主在没有强大工具支持的情况下很难做出合理的竞价。因此，人们进行了大量的研究来帮助广告商了解这种动态的、无限的、不透明的拍卖情况，并获得尽可能好的竞价结果。本文专注于预测关键字组合的投资回报 (ROI)，开发了分层贝叶斯网络 (BN) 模型来预测关键字拍卖的绩效。很少有论文直接预测关键词组合的投资回报率。这种方法通过选择正确的关键字和出价来实现期望的结果，有效地呼应了广告商对关键字拍卖的期望。预测模型的构建块（例如出价和排名）以具有一组联合条件概率的树形结构进行组织。为了推断预测变量的后验概率，在验证网络的结构关系后执行贝叶斯参数学习算法。实证研究表明，该预测模型对于关键词拍卖是合适且有效的。此外，所提出的分层 BN 模型比流行的预测方法——反向传播人工神经网络显示出更高的精度。
Prosperity of the online keyword auctions greatly facilitates the penetration of search-engine marketing in various industries. However, the current operation rules of the search engine make it very difficult for those inexperienced advertisers to make sound bids without the support of powerful tools. Therefore, many studies have been conducted to help advertisers understand such dynamic, infinite, and opaque auction situation, and obtain as good as possible bidding result. This paper, focusing on predicting the return on investment (ROI) of a keyword portfolio, develops a hierarchical Bayesian network (BN) model to forecast keyword auctions' performance. Few papers directly predict the ROI of a keyword portfolio. This approach effectively echoes advertisers' expectation for a keyword auction by choosing the right keywords and bids to achieve the desired outcome. The building blocks of the prediction model, such as bid and rank, are organized in a tree-shaped structure with a set of joint conditional probabilities. To infer the posterior probabilities of the predictors, a Bayesian parameter-learning algorithm is conducted after validating the network's structural relationships. The empirical study demonstrates that the prediction model is appropriate and effective for keyword auctions. Moreover, the proposed hierarchical BN model shows a higher accuracy than the popular prediction approach-the back-propagation artificial neural network.