# -*- coding: utf-8 -*-
# @Time : 2020/12/14
# @Author : Yihong Guo
# @Email : gyihong@hotmail.com
r"""
MultiVAE
################################################
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 AutoEncoderMixin, GeneralRecommender
from recbole.model.init import xavier_normal_initialization
from recbole.utils import InputType
[docs]class MultiVAE(GeneralRecommender, AutoEncoderMixin):
r"""MultiVAE is an item-based collaborative filtering model that simultaneously ranks all items for each user.
We implement the MultiVAE model with only user dataloader.
"""
input_type = InputType.PAIRWISE
def __init__(self, config, dataset):
super(MultiVAE, self).__init__(config, dataset)
self.layers = config["mlp_hidden_size"]
self.lat_dim = config["latent_dimension"]
self.drop_out = config["dropout_prob"]
self.anneal_cap = config["anneal_cap"]
self.total_anneal_steps = config["total_anneal_steps"]
self.build_histroy_items(dataset)
self.update = 0
self.encode_layer_dims = [self.n_items] + self.layers + [self.lat_dim]
self.decode_layer_dims = [int(self.lat_dim / 2)] + self.encode_layer_dims[::-1][
1:
]
self.encoder = self.mlp_layers(self.encode_layer_dims)
self.decoder = self.mlp_layers(self.decode_layer_dims)
# parameters initialization
self.apply(xavier_normal_initialization)
[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 reparameterize(self, mu, logvar):
if self.training:
std = torch.exp(0.5 * logvar)
epsilon = torch.zeros_like(std).normal_(mean=0, std=0.01)
return mu + epsilon * std
else:
return mu
[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)
mu = h[:, : int(self.lat_dim / 2)]
logvar = h[:, int(self.lat_dim / 2) :]
z = self.reparameterize(mu, logvar)
z = self.decoder(z)
return z, mu, logvar
[docs] def calculate_loss(self, interaction):
user = interaction[self.USER_ID]
rating_matrix = self.get_rating_matrix(user)
self.update += 1
if self.total_anneal_steps > 0:
anneal = min(self.anneal_cap, 1.0 * self.update / self.total_anneal_steps)
else:
anneal = self.anneal_cap
z, mu, logvar = self.forward(rating_matrix)
# KL loss
kl_loss = (
-0.5
* torch.mean(torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=1))
* anneal
)
# CE loss
ce_loss = -(F.log_softmax(z, 1) * rating_matrix).sum(1).mean()
return ce_loss + kl_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[[torch.arange(len(item)).to(self.device), 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)