From 27fd5d6e4981c3637bf1c59fa14ca203bab97d31 Mon Sep 17 00:00:00 2001 From: julie Date: Sat, 16 Sep 2017 02:31:17 -0700 Subject: [PATCH] CTR: add English README --- ctr/README.md | 18 +----------------- ctr/index.html | 18 +----------------- 2 files changed, 2 insertions(+), 34 deletions(-) diff --git a/ctr/README.md b/ctr/README.md index ab757bbc..419abc86 100644 --- a/ctr/README.md +++ b/ctr/README.md @@ -1,24 +1,8 @@ # Click-Through Rate Prediction -以下是本例目录包含的文件以及对应说明: - -``` -├── README.md # 本教程markdown 文档 -├── dataset.md # 数据集处理教程 -├── images # 本教程图片目录 -│   ├── lr_vs_dnn.jpg -│   └── wide_deep.png -├── infer.py # 预测脚本 -├── network_conf.py # 模型网络配置 -├── reader.py # data reader -├── train.py # 训练脚本 -└── utils.py # helper functions -└── avazu_data_processer.py # 示例数据预处理脚本 -``` - ## Introduction -CTR(Click-Through Rate，点击率预估)\[[1](https://en.wikipedia.org/wiki/Click-through_rate)\] +CTR(Click-Through Rate)\[[1](https://en.wikipedia.org/wiki/Click-through_rate)\] is a prediction of the probability that a user clicks on an advertisement. This model is widely used in the advertisement industry. Accurate click rate estimates are important for maximizing online advertising revenue. When there are multiple ad slots, CTR estimates are generally used as a baseline for ranking. For example, in a search engine's ad system, when the user enters a query, the system typically performs the following steps to show relevant ads. diff --git a/ctr/index.html b/ctr/index.html index 2ae045dc..17022e45 100644 --- a/ctr/index.html +++ b/ctr/index.html @@ -42,25 +42,9 @@