deepfm内容理解

对于CTR问题，被证明的最有效的提升任务表现的策略是特征组合(Feature Interaction)；

两个问题：

如何更好地学习特征组合，进而更加精确地描述数据的特点；

如何更高效的学习特征组合。

DNN局限 ：当我们使用DNN网络解决推荐问题的时候存在网络参数过于庞大的问题，这是因为在进行特征处理的时候我们需要使用one-hot编码来处理离散特征，这会导致输入的维度猛增。

为了解决DNN参数量过大的局限性，可以采用非常经典的Field思想，将OneHot特征转换为Dense Vector，通过增加全连接层就可以实现高阶的特征组合。

黑色的线和红色的线进行concat

self定义

deep_features = deep_features
fm_features = fm_features #稀疏的特征
deep_dims = sum([fea.embed_dim for fea in deep_features]) #8
fm_dims = sum([fea.embed_dim for fea in fm_features]) #368 = 23*16 #稀疏的特征embedding化
linear = LR(fm_dims) # 1-odrder interaction 低阶信息 (fc): Linear(in_features=368, out_features=1, bias=True)
fm = FM(reduce_sum=True) # 2-odrder interaction #FM将一阶特征和二阶特征cancat
embedding = EmbeddingLayer(deep_features + fm_features)
mlp = MLP(deep_dims, **mlp_params)

forward

input_deep = embedding(x, deep_features, squeeze_dim=True) #[batch_size, deep_dims] torch.Size([10, 8])
input_fm = embedding(x, fm_features, squeeze_dim=False) #[batch_size, num_fields, embed_dim] torch.Size([10, 23, 16])
y_linear = linear(input_fm.flatten(start_dim=1)) #torch.Size([10, 1]) 对应的稀疏特征经过线性层变为1
y_fm = fm(input_fm) #torch.Size([10, 1]) #对稀疏特征做一阶二阶处理
y_deep = mlp(input_deep) #[batch_size, 1] #torch.Size([10, 1])
y = y_linear + y_fm + y_deep
# return torch.sigmoid(y.squeeze(1))

定义的一些函数：

import torch.nn as nn
class LR(nn.Module):
"""Logistic Regression Module. It is the one Non-linear
transformation for input feature.

Args:
input_dim (int): input size of Linear module.
sigmoid (bool): whether to add sigmoid function before output.

Shape:
- Input: `(batch_size, input_dim)`
- Output: `(batch_size, 1)`
"""

def __init__(self, input_dim, sigmoid=False):
super().__init__()
self.sigmoid = sigmoid
self.fc = nn.Linear(input_dim, 1, bias=True)

def forward(self, x):
if self.sigmoid:
return torch.sigmoid(self.fc(x))
else:
return self.fc(x)

class FM(nn.Module):
"""The Factorization Machine module, mentioned in the `DeepFM paper
<https://arxiv.org/pdf/1703.04247.pdf>`. It is used to learn 2nd-order
feature interactions.

Args:
reduce_sum (bool): whether to sum in embed_dim (default = `True`).

Shape:
- Input: `(batch_size, num_features, embed_dim)`
- Output: `(batch_size, 1)`` or ``(batch_size, embed_dim)`
"""

def __init__(self, reduce_sum=True):
super().__init__()
self.reduce_sum = reduce_sum

def forward(self, x):
square_of_sum = torch.sum(x, dim=1)**2
sum_of_square = torch.sum(x**2, dim=1)
ix = square_of_sum - sum_of_square
if self.reduce_sum:
ix = torch.sum(ix, dim=1, keepdim=True)
return 0.5 * ix