Electronic Commerce Research ( IF 3.462 ) Pub Date : 2021-03-28 , DOI: 10.1007/s10660-021-09472-1 Maryam Almasharawi , Ahmet Bulut
Advertising to search engine users is a primary medium of online advertising. It is the largest source of revenue for search engines. Performance-driven advertising is essential for advertisers and search engines alike. The user response rate in search advertising refers to the observed rate of a desired user action such as click-through or conversion. To estimate the response rate, we built a near-neighbor based data extrapolation method called RespRate-LSH using locality sensitive hashing (LSH). The target response rate is estimated as the weighted average of the response rates of near neighbors identified via LSH. The hyper-parameters of RespRate-LSH were studied in detail, and its empirical performance was compared with traditional machine learning methods and with deep neural networks. RespRate-LSH showed exemplary performance.
中文翻译:
在搜索营销中使用局部敏感哈希估算用户响应率
向搜索引擎用户投放广告是在线广告的主要媒介。它是搜索引擎收入的最大来源。效果驱动型广告对于广告客户和搜索引擎都是至关重要的。搜索广告中的用户响应率是指期望的用户操作(例如点击或转化)的观察到的速率。为了估计响应率,我们使用局部敏感哈希(LSH)建立了一种基于近邻的数据外推方法,称为RespRate-LSH。目标响应率估计为通过LSH识别的近邻的响应率的加权平均值。详细研究了RespRate-LSH的超参数,并将其经验性能与传统的机器学习方法和深度神经网络进行了比较。RespRate-LSH显示出出色的性能。