前言：应该只要一天时间，先看Google英文论文，配合中文翻译：http://d0evi1.com/widedeep-recsys，再看些官方的代码，需要先了解下tensorflow的feature API：
tf.contrib.layers 中提供了一系列的函数定义不同类型的 feature columns：
tf.contrib.layers.sparse_column_with_XXX 构建低维离散特征
sparse_feature_a = sparse_column_with_hash_bucket(…)
sparse_feature_b = sparse_column_with_hash_bucket(…)
tf.contrib.layers.crossed_column 构建离散特征的组合
sparse_feature_a_x_sparse_feature_b = crossed_column([sparse_feature_a, sparse_feature_b], …)
tf.contrib.layers.real_valued_column 构建连续型实数特征
real_feature_a = real_valued_column(…)
tf.contrib.layers.embedding_column 构建 embedding 特征
sparse_feature_a_emb = embedding_column(sparse_id_column=sparse_feature_a, )
feature_column API：
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/feature_column/feature_column_lib.py
1）Google 官方资料：
wide and deep论文：
https://arxiv.org/pdf/1606.07792.pdf
wide and depp 主函数：https://github.com/tensorflow/tensorflow/blob/b474e55c23e5cc42b01a1ddea34751f01110deb6/tensorflow/python/estimator/canned/dnn_linear_combined.py
tf.estimator.DNNLinearCombinedClassifier主函数参数：
https://www.w3cschool.cn/tensorflow_python/tensorflow_python-4gn82dty.html
wide_n_deep_tutorial.py代码例子：https://github.com/tensorflow/tensorflow/blob/88abddbc30a34855d8cdf44c81eac388fbec955b/tensorflow/examples/learn/wide_n_deep_tutorial.py
wide_and_deep.md：
https://github.com/tensorflow/tensorflow/blob/752dcb61ef7a8fd6555909dc37c1f2a2e5792227/tensorflow/docs_src/tutorials/wide_and_deep.md
TensorFlow Linear Model Tutorial：
https://github.com/tensorflow/tensorflow/blob/752dcb61ef7a8fd6555909dc37c1f2a2e5792227/tensorflow/docs_src/tutorials/wide.md
2）非官网参考资料：
TensorFlow下的大规模线性模型：
http://www.jianshu.com/p/afe41ae17a16
tensorflow逻辑回归：http://blog.csdn.net/willduan1/article/details/53105603
tensorlfow线性模型
http://shuaizhang.tech/2016/12/17/Tensorflow-Tutorial-5-Linear-Model-%E7%BA%BF%E6%80%A7%E6%A8%A1%E5%9E%8B/
wide and depp 论文中文翻译：
基于Wide & Deep Learning的推荐系统http://d0evi1.com/widedeep-recsys/
Summary Of recommender system surveys in recent years：
http://shuaizhang.tech/2017/07/28/Summary-of-Recommender-System-Surveys-in-recent-years/
各大公司广泛使用的在线学习算法FTRL详解：
http://www.cnblogs.com/EE-NovRain/p/3810737.html
Tensorflow - wide and deep.ipynb：
https://github.com/bartgras/XGBoost-Tensorflow-Wide-and-deep-comparison/blob/master/Tensorflow%20-%20wide%20and%20deep.ipynb
TensorFlow Wide And Deep 模型详解与应用：
http://geek.csdn.net/news/detail/235465
TensorFlow Wide And Deep 模型详解与应用（二）：
http://geek.csdn.net/news/detail/235471
tensorflow线性模型以及Wide deep learning：
http://blog.csdn.net/zhangshuaizaxia/article/details/53707301
TensorFlow 广度和深度学习教程：
http://www.jianshu.com/p/7dc588d98a94
深度学习在美团点评推荐平台排序中的应用&& wide&&deep推荐系统模型--学习笔记：
http://www.cnblogs.com/wuxiangli/p/7258474.html
Google_Wide&Deep Learning
http://www.datakit.cn/blog/2016/08/21/wdnn.html
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