Deep Learning for Click-Through Rate Estimation

Weinan Zhang, Jiarui Qin, Wei Guo, Ruiming Tang, Xiuqiang He

Shanghai Jiao Tong University, Huawei Noah’s Ark Lab

https://arxiv.org/pdf/2104.10584.pdf

点击率预估在很多个性化在线服务中起着非常重要的作用，是一个重要模块，比如在线广告，推荐系统，网络搜索等。

自从2015年起，深度学习的成功开始用于提升点击率预估的效果，现在深度点击率模型已经广泛用于很多工业平台中。这篇综述会综合回顾点击率预估中的深度学习模型。首先回顾从浅层到深层点击率预估模型的转变，然后解释模型往深层发展趋势的必要性。

然后。简介ctr模型中显式特征交互模块。基于丰富的用户历史和大平台，介绍深层行为模型。最近兴起的自动设计深层点击率模型结构会简单介绍下。

多个域的类别数据示例如下

目标函数形式如下

点击率预估模型发展历史图示如下

单塔和双塔的ctr预估模型结构对比如下

几种典型的特征交互算子图示如下

用户行为建模图示如下

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