khanhnamle1994

import torch

from layer import FactorizationMachine, FeaturesEmbedding, MultiLayerPerceptron, FeaturesLinear


class NeuralFactorizationMachineModel(torch.nn.Module):
    """
    A Pytorch implementation of Neural Factorization Machine.
    Reference:
        X He and TS Chua, Neural Factorization Machines for Sparse Predictive Analytics, 2017.

khanhnamle1994

import torch

from layer import CompressedInteractionNetwork, FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron


class ExtremeDeepFactorizationMachineModel(torch.nn.Module):
    """
    A Pytorch implementation of xDeepFM.
    Reference:
        J Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018.

khanhnamle1994

import torch

from layer import FactorizationMachine, FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron


class DeepFactorizationMachineModel(torch.nn.Module):
    """
    A Pytorch implementation of DeepFM.
    Reference:
        H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.

khanhnamle1994

import torch

from layer import FeaturesLinear, FeaturesEmbedding, MultiLayerPerceptron


class WideAndDeepModel(torch.nn.Module):
    """
    A Pytorch implementation of wide and deep learning.
    Reference:
        HT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016.

khanhnamle1994

import torch
from torch import nn
import torch.nn.functional as F

class MF(nn.Module):
  
  def __call__(self, train_x):

        # These are the user indices, and correspond to "u" variable
        user_id = train_x[:, 0]

khanhnamle1994

import torch
from torch import nn
import torch.nn.functional as F

class MF(nn.Module):
  
  def __call__(self, train_x):

        # These are the user indices, and correspond to "u" variable
        user_id = train_x[:, 0]

khanhnamle1994

import torch
from torch import nn
import torch.nn.functional as F

class MF(nn.Module):
  
  def __call__(self, train_x):

        # These are the user indices, and correspond to "u" variable
        user_id = train_x[:, 0]

khanhnamle1994

import torch
from torch import nn
import torch.nn.functional as F

class MF(nn.Module):
  
  def __call__(self, train_x):

        # These are the user indices, and correspond to "u" variable
        user_id = train_x[:, 0]

khanhnamle1994

import torch
from torch import nn
import torch.nn.functional as F

class MF(nn.Module):
  
  def __call__(self, train_x):

        # Pull out biases
        biases = index_into(self.bias_feat.weight, train_x).squeeze().sum(dim=1)

khanhnamle1994

import torch
from torch import nn
import torch.nn.functional as F

class MF(nn.Module):
  
  def __call__(self, train_x):

        # These are the user and item indices
        user_id = train_x[:, 0]