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.
1.获取与用户搜索词相关的广告集合
1.Get the ad collection associated with the user's search term.
2.业务规则和相关性过滤
2.Business rules and relevance filtering.
3.根据拍卖机制和 CTR 排序
3.Rank by auction mechanism and CTR.
4.展出广告
4.Show ads.
可以看到，CTR 在最终排序中起到了很重要的作用。
Here，CTR plays a crucial role.
### 发展阶段
### Brief history
在业内，CTR 模型经历了如下的发展阶段：
Historically, the CTR prediction model has been evolving as follows.
LR 的蓝色箭头部分可以直接类比到 DNN 中对应的结构，可以看到 LR 和 DNN 有一些共通之处（比如权重累加），
We can see, LR and CNN have some common structures. However, DNN can have non-linear relation between input and output values by adding activation unit and further layers. This enables DNN to achieve better learning results in CTR estimates.
-`test_set_size`The number of rows for the test set
-`train_size`生成训练姐的行数
-`train_size`The number of rows of training set
## Wide & Deep Learning Model
## Wide & Deep Learning Model
谷歌在 16 年提出了 Wide & Deep Learning 的模型框架，用于融合适合学习抽象特征的 DNN 和 适用于大规模稀疏特征的 LR 两种模型的优点。
Google proposed a model framework for Wide & Deep Learning to integrate the advantages of both DNNs suitable for learning abstract features and LR models for large sparse features.
### 模型简介
### Introduction to the model
Wide & Deep Learning Model\[[3](#参考文献)\] 可以作为一种相对成熟的模型框架使用，
Wide & Deep Learning Model\[[3](#References)\] is a relatively mature model, but this model is still being used in the CTR predicting task. Here we demonstrate the use of this model to complete the CTR predicting task.
The wide part of the left side of the model can accommodate large-scale coefficient features and has some memory for some specific information (such as ID); and the Deep part of the right side of the model can learn the implicit relationship between features.
3. Cheng H T, Koc L, Harmsen J, et al. [Wide & deep learning for recommender systems](https://arxiv.org/pdf/1606.07792.pdf)[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.
3. Cheng H T, Koc L, Harmsen J, et al. [Wide & deep learning for recommender systems](https://arxiv.org/pdf/1606.07792.pdf)[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.
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.
1. 获取与用户搜索词相关的广告集合
1. Get the ad collection associated with the user's search term.
2. 业务规则和相关性过滤
2. Business rules and relevance filtering.
3. 根据拍卖机制和 CTR 排序
3. Rank by auction mechanism and CTR.
4. 展出广告
4. Show ads.
可以看到，CTR 在最终排序中起到了很重要的作用。
Here，CTR plays a crucial role.
### 发展阶段
### Brief history
在业内，CTR 模型经历了如下的发展阶段：
Historically, the CTR prediction model has been evolving as follows.
LR 的蓝色箭头部分可以直接类比到 DNN 中对应的结构，可以看到 LR 和 DNN 有一些共通之处（比如权重累加），
We can see, LR and CNN have some common structures. However, DNN can have non-linear relation between input and output values by adding activation unit and further layers. This enables DNN to achieve better learning results in CTR estimates.
- `num_lines_to_detect` The number of generated IDs
- `test_set_size` 生成测试集的行数
- `test_set_size` The number of rows for the test set
- `train_size` 生成训练姐的行数
- `train_size` The number of rows of training set
## Wide & Deep Learning Model
## Wide & Deep Learning Model
谷歌在 16 年提出了 Wide & Deep Learning 的模型框架，用于融合适合学习抽象特征的 DNN 和 适用于大规模稀疏特征的 LR 两种模型的优点。
Google proposed a model framework for Wide & Deep Learning to integrate the advantages of both DNNs suitable for learning abstract features and LR models for large sparse features.
### 模型简介
### Introduction to the model
Wide & Deep Learning Model\[[3](#参考文献)\] 可以作为一种相对成熟的模型框架使用，
Wide & Deep Learning Model\[[3](#References)\] is a relatively mature model, but this model is still being used in the CTR predicting task. Here we demonstrate the use of this model to complete the CTR predicting task.
The wide part of the left side of the model can accommodate large-scale coefficient features and has some memory for some specific information (such as ID); and the Deep part of the right side of the model can learn the implicit relationship between features.
3. Cheng H T, Koc L, Harmsen J, et al. [Wide & deep learning for recommender systems](https://arxiv.org/pdf/1606.07792.pdf)[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.
3. Cheng H T, Koc L, Harmsen J, et al. [Wide & deep learning for recommender systems](https://arxiv.org/pdf/1606.07792.pdf)[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.
