推荐系统——An Embedding Learning Framework for Numerical Features in CTR Prediction

前言

论文：https://arxiv.org/pdf/2012.08986.pdf
收录：KDD‘21
机构：华为

论文解读

这个老哥写的真的很好了，点这里：知乎

代码

论文的代码链接挂了并且源码我看了是华为mindxxx啥深度学习框架写的，而且看了看跟论文的公式也没对齐…干脆我就对着论文用pytorch写了一份

import torch
import torch.nn as nn


class AudisEconder(nn.Module):
    r"""Args: in_dim: the dimension of input tensor out_dim: the dimension of output tensor H_j: the number of Meta_embeddings alpha: the factor of skip-connection t: Temperature Coefficient """
    def __init__(self, in_dim, out_dim, H_j=20, alpha=0.1, t=1e-5):

        super(AudisEconder, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.w_j = nn.Linear(in_dim, H_j)
        self.leak_relu = nn.LeakyReLU()
        self.W_j = nn.Linear(H_j, H_j)
        self.alpha = alpha
        self.t = t
        self.softmax = nn.Softmax(dim=-1)
        self.ME = nn.Parameter(torch.randn(H_j, out_dim))

    def forward(self, x):
        h_j = self.leak_relu(self.w_j(x))
        x_hat_j = self.W_j(h_j) + self.alpha * h_j
        x_hat_j_h = self.softmax(x_hat_j / self.t)
        e_j = x_hat_j_h @ self.ME
        return e_j



if __name__ == '__main__':
    input = torch.rand(16, 10)
    model = AudisEconder(in_dim=10, out_dim=128)
    out = model(input)
    print(out.shape)