前言
最近看了2018年阿里在KDD上发表的论文《Deepfor Click- Rate 》 ， 想复现下 ， 看了文章给出的开源代码 ， 发现环境是TF1.4的 ， 并且注释太少 ， 有些没大理解【还是太菜了】 ， 因此准备参考原有代码使用TF2.0来对模型进行简单的复现 。如果有些地方有些出入或者错误 ， 请大佬们给我指出 ， 感谢【因为现在没服务器 ， 所以没像开源中跑完50个epoch】
数据分析
1、数据集为论文中的  ， 下载并解压：
wget -c http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
gzip -d reviews_Electronics_5.json.gz
wget -c http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/meta_Electronics.json.gz
gzip -d meta_Electronics.json.gz

其中5.json为用户的行为数据 ， 为广告的元数据 。
某单个样本如下：
{"reviewerID": "A2SUAM1J3GNN3B","asin": "0000013714","reviewerName": "J. McDonald","helpful": [2, 3],"reviewText": "I bought this for my husband who plays the piano.He is having a wonderful time playing these old hymns.The musicis at times hard to read because we think the book was published for singing from more than playing from.Great purchase though!","overall": 5.0,"summary": "Heavenly Highway Hymns","unixReviewTime": 1252800000,"reviewTime": "09 13, 2009"}
各字段分别为：
meta某样本如下：
{
"asin": "0000031852",
"title": "Girls Ballet Tutu Zebra Hot Pink",
"price": 3.17,
"imUrl": "http://ecx.images-amazon.com/images/I/51fAmVkTbyL._SY300_.jpg",
"related":
{
"also_bought": ["B00JHONN1S", "B002BZX8Z6", "B00D2K1M3O", "0000031909", "B00613WDTQ", "B00D0WDS9A", "B00D0GCI8S", "0000031895", "B003AVKOP2", "B003AVEU6G", "B003IEDM9Q", "B002R0FA24", "B00D23MC6W", "B00D2K0PA0", "B00538F5OK", "B00CEV86I6", "B002R0FABA", "B00D10CLVW", "B003AVNY6I", "B002GZGI4E", "B001T9NUFS", "B002R0F7FE", "B00E1YRI4C", "B008UBQZKU", "B00D103F8U", "B007R2RM8W"],
"also_viewed": ["B002BZX8Z6", "B00JHONN1S", "B008F0SU0Y", "B00D23MC6W", "B00AFDOPDA", "B00E1YRI4C", "B002GZGI4E", "B003AVKOP2", "B00D9C1WBM", "B00CEV8366", "B00CEUX0D8", "B0079ME3KU", "B00CEUWY8K", "B004FOEEHC", "0000031895", "B00BC4GY9Y", "B003XRKA7A", "B00K18LKX2", "B00EM7KAG6", "B00AMQ17JA", "B00D9C32NI", "B002C3Y6WG", "B00JLL4L5Y", "B003AVNY6I", "B008UBQZKU", "B00D0WDS9A", "B00613WDTQ", "B00538F5OK", "B005C4Y4F6", "B004LHZ1NY", "B00CPHX76U", "B00CEUWUZC", "B00IJVASUE", "B00GOR07RE", "B00J2GTM0W", "B00JHNSNSM", "B003IEDM9Q", "B00CYBU84G", "B008VV8NSQ", "B00CYBULSO", "B00I2UHSZA", "B005F50FXC", "B007LCQI3S", "B00DP68AVW", "B009RXWNSI", "B003AVEU6G", "B00HSOJB9M", "B00EHAGZNA", "B0046W9T8C", "B00E79VW6Q", "B00D10CLVW", "B00B0AVO54", "B00E95LC8Q", "B00GOR92SO", "B007ZN5Y56", "B00AL2569W", "B00B608000", "B008F0SMUC", "B00BFXLZ8M"],
"bought_together": ["B002BZX8Z6"]
},
"salesRank": {"Toys & Games": 211836},
"brand": "Coxlures",
"categories": [["Sports & Outdoors", "Other Sports", "Dance"]]
}

各字段分别为：
2、首先将原生数据存储的json格式转化为数据流格式 ， 方便读取：
def to_df(file_path):
"""
转化为DataFrame结构
:param file_path: 文件路径
:return:
"""
with open(file_path, 'r') as fin:
df = {}
i = 0
for line in fin:
df[i] = eval(line)
i += 1
df = pd.DataFrame.from_dict(df, orient='index')
return df
reviews_df = to_df('../raw_data/reviews_Electronics_5.json')
# 可以直接调用pandas的read_json方法 ， 但会改变列的顺序