From ef1e546f0822c2588e27798c78a4a70f5e396ce8 Mon Sep 17 00:00:00 2001 From: wangmeng28 Date: Mon, 27 Nov 2017 21:59:52 +0800 Subject: [PATCH] Add index to deepfm in README --- README.cn.md | 3 ++- README.md | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/README.cn.md b/README.cn.md index 8806dbfa..fe80229f 100644 --- a/README.cn.md +++ b/README.cn.md @@ -29,9 +29,10 @@ PaddlePaddle提供了丰富的运算单元，帮助大家以模块化的方式 点击率预估模型预判用户对一条广告点击的概率，对每次广告的点击情况做出预测，是广告技术的核心算法之一。逻谛斯克回归对大规模稀疏特征有着很好的学习能力，在点击率预估任务发展的早期一统天下。近年来，DNN 模型由于其强大的学习能力逐渐接过点击率预估任务的大旗。 -在点击率预估任务中，我们给出谷歌提出的 Wide & Deep 模型。这一模型融合了适用于学习抽象特征的DNN和适用于大规模稀疏特征的逻谛斯克回归两者的优点，可以作为一种相对成熟的模型框架使用，在工业界也有一定的应用。 +在点击率预估任务中，我们首先给出谷歌提出的 Wide & Deep 模型。这一模型融合了适用于学习抽象特征的DNN和适用于大规模稀疏特征的逻谛斯克回归两者的优点，可以作为一种相对成熟的模型框架使用，在工业界也有一定的应用。同时，我们提供基于因子分解机的深度神经网络模型，该模型融合了因子分解机和深度神经网络，分别建模输入属性之间的低阶交互和高阶交互。 - 3.1 [Wide & deep 点击率预估模型](https://github.com/PaddlePaddle/models/tree/develop/ctr) +- 3.2 [基于深度因子分解机的点击率预估模型](https://github.com/PaddlePaddle/models/tree/develop/deep_fm) ## 4. 文本分类 diff --git a/README.md b/README.md index 3b2da82a..8b938a30 100644 --- a/README.md +++ b/README.md @@ -25,9 +25,10 @@ The language model is important in the field of natural language processing. In ## 3. Click-Through Rate prediction The click-through rate model predicts the probability that a user will click on an ad. This is widely used for advertising technology. Logistic Regression has a good learning performance for large-scale sparse features in the early stages of the development of click-through rate prediction. In recent years, DNN model because of its strong learning ability to gradually take the banner rate of the task of the banner. -In the example of click-through rate estimates, we give the Google's Wide & Deep model. This model combines the advantages of DNN and the applicable logistic regression model for DNN and large-scale sparse features. +In the example of click-through rate estimates, we first give the Google's Wide & Deep model. This model combines the advantages of DNN and the applicable logistic regression model for DNN and large-scale sparse features. Then we provide the deep factorization machine for click-through rate prediction. The deep factorization machine combines the factorization machine and deep neural networks to model both low order and high order interactions of input features. - 3.1 [Click-Through Rate Model](https://github.com/PaddlePaddle/models/tree/develop/ctr) +- 3.2 [Deep Factorization Machine for Click-Through Rate prediction](https://github.com/PaddlePaddle/models/tree/develop/deep_fm) ## 4. Text classification -- GitLab