# -*- coding: utf-8 -*-
# @Time : 2020/12/14
# @Author : Yihong Guo
# @Email : gyihong@hotmail.com
r"""
MultiDAE
################################################
Reference:
Dawen Liang et al. "Variational Autoencoders for Collaborative Filtering." in WWW 2018.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from recbole.model.abstract_recommender import GeneralRecommender
from recbole.model.init import xavier_normal_initialization
from recbole.model.layers import MLPLayers
from recbole.utils import InputType
[docs]class MultiDAE(GeneralRecommender):
r"""MultiDAE is an item-based collaborative filtering model that simultaneously ranks all items for each user.
We implement the the MultiDAE model with only user dataloader.
"""
input_type = InputType.PAIRWISE
def __init__(self, config, dataset):
super(MultiDAE, self).__init__(config, dataset)
self.layers = config["mlp_hidden_size"]
self.lat_dim = config['latent_dimension']
self.drop_out = config['dropout_prob']
self.history_item_id, self.history_item_value, _ = dataset.history_item_matrix()
self.history_item_id = self.history_item_id.to(self.device)
self.history_item_value = self.history_item_value.to(self.device)
self.encode_layer_dims = [self.n_items] + self.layers + [self.lat_dim]
self.decode_layer_dims = [self.lat_dim] + self.encode_layer_dims[::-1][1:]
self.encoder = MLPLayers(self.encode_layer_dims, activation='tanh')
self.decoder = self.mlp_layers(self.decode_layer_dims)
# parameters initialization
self.apply(xavier_normal_initialization)
[docs] def get_rating_matrix(self, user):
r"""Get a batch of user's feature with the user's id and history interaction matrix.
Args:
user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ]
Returns:
torch.FloatTensor: The user's feature of a batch of user, shape: [batch_size, n_items]
"""
# Following lines construct tensor of shape [B,n_items] using the tensor of shape [B,H]
col_indices = self.history_item_id[user].flatten()
row_indices = torch.arange(user.shape[0]).to(self.device) \
.repeat_interleave(self.history_item_id.shape[1], dim=0)
rating_matrix = torch.zeros(1).to(self.device).repeat(user.shape[0], self.n_items)
rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten())
return rating_matrix
[docs] def mlp_layers(self, layer_dims):
mlp_modules = []
for i, (d_in, d_out) in enumerate(zip(layer_dims[:-1], layer_dims[1:])):
mlp_modules.append(nn.Linear(d_in, d_out))
if i != len(layer_dims[:-1]) - 1:
mlp_modules.append(nn.Tanh())
return nn.Sequential(*mlp_modules)
[docs] def forward(self, rating_matrix):
h = F.normalize(rating_matrix)
h = F.dropout(h, self.drop_out, training=self.training)
h = self.encoder(h)
return self.decoder(h)
[docs] def calculate_loss(self, interaction):
user = interaction[self.USER_ID]
rating_matrix = self.get_rating_matrix(user)
z = self.forward(rating_matrix)
# CE loss
ce_loss = -(F.log_softmax(z, 1) * rating_matrix).sum(1).mean()
return ce_loss
[docs] def predict(self, interaction):
user = interaction[self.USER_ID]
item = interaction[self.ITEM_ID]
rating_matrix = self.get_rating_matrix(user)
scores = self.forward(rating_matrix)
return scores[[user, item]]
[docs] def full_sort_predict(self, interaction):
user = interaction[self.USER_ID]
rating_matrix = self.get_rating_matrix(user)
scores = self.forward(rating_matrix)
return scores.view(-1)