Source code for recbole.model.general_recommender.multidae

# -*- 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[[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)