Source code for recbole.model.sequential_recommender.gru4recf

# -*- coding: utf-8 -*-
# @Time    : 2020/9/14 16:57
# @Author  : Hui Wang
# @Email   : hui.wang@ruc.edu.cn

r"""
GRU4RecF
################################################

Reference:
    Bal√°zs Hidasi et al. "Parallel Recurrent Neural Network Architectures for
    Feature-rich Session-based Recommendations." in RecSys 2016.

"""

import torch
from torch import nn

from recbole.model.abstract_recommender import SequentialRecommender
from recbole.model.init import xavier_normal_initialization
from recbole.model.layers import FeatureSeqEmbLayer
from recbole.model.loss import BPRLoss


[docs]class GRU4RecF(SequentialRecommender): r""" In the original paper, the authors proposed several architectures. We compared 3 different architectures: (1) Concatenate item input and feature input and use single RNN, (2) Concatenate outputs from two different RNNs, (3) Weighted sum of outputs from two different RNNs. We implemented the optimal parallel version(2), which uses different RNNs to encode items and features respectively and concatenates the two subparts' outputs as the final output. The different RNN encoders are trained simultaneously. """ def __init__(self, config, dataset): super(GRU4RecF, self).__init__(config, dataset) # load parameters info self.embedding_size = config['embedding_size'] self.hidden_size = config['hidden_size'] self.num_layers = config['num_layers'] self.dropout_prob = config['dropout_prob'] self.selected_features = config['selected_features'] self.pooling_mode = config['pooling_mode'] self.device = config['device'] self.num_feature_field = len(config['selected_features']) self.loss_type = config['loss_type'] # define layers and loss self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) self.feature_embed_layer = FeatureSeqEmbLayer( dataset, self.embedding_size, self.selected_features, self.pooling_mode, self.device ) self.item_gru_layers = nn.GRU( input_size=self.embedding_size, hidden_size=self.hidden_size, num_layers=self.num_layers, bias=False, batch_first=True, ) # For simplicity, we use same architecture for item_gru and feature_gru self.feature_gru_layers = nn.GRU( input_size=self.embedding_size * self.num_feature_field, hidden_size=self.hidden_size, num_layers=self.num_layers, bias=False, batch_first=True, ) self.dense_layer = nn.Linear(self.hidden_size * 2, self.embedding_size) self.dropout = nn.Dropout(self.dropout_prob) if self.loss_type == 'BPR': self.loss_fct = BPRLoss() elif self.loss_type == 'CE': self.loss_fct = nn.CrossEntropyLoss() else: raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") # parameters initialization self.apply(xavier_normal_initialization)
[docs] def forward(self, item_seq, item_seq_len): item_seq_emb = self.item_embedding(item_seq) item_seq_emb_dropout = self.dropout(item_seq_emb) item_gru_output, _ = self.item_gru_layers(item_seq_emb_dropout) # [B Len H] sparse_embedding, dense_embedding = self.feature_embed_layer(None, item_seq) sparse_embedding = sparse_embedding['item'] dense_embedding = dense_embedding['item'] # concat the sparse embedding and float embedding feature_table = [] if sparse_embedding is not None: feature_table.append(sparse_embedding) if dense_embedding is not None: feature_table.append(dense_embedding) feature_table = torch.cat(feature_table, dim=-2) # [batch len num_features hidden_size] table_shape = feature_table.shape feat_num, embedding_size = table_shape[-2], table_shape[-1] feature_emb = feature_table.view(table_shape[:-2] + (feat_num * embedding_size,)) feature_gru_output, _ = self.feature_gru_layers(feature_emb) # [B Len H] output_concat = torch.cat((item_gru_output, feature_gru_output), -1) # [B Len 2*H] output = self.dense_layer(output_concat) output = self.gather_indexes(output, item_seq_len - 1) # [B H] return output # [B H]
[docs] def calculate_loss(self, interaction): item_seq = interaction[self.ITEM_SEQ] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(item_seq, item_seq_len) pos_items = interaction[self.POS_ITEM_ID] if self.loss_type == 'BPR': neg_items = interaction[self.NEG_ITEM_ID] pos_items_emb = self.item_embedding(pos_items) # [B H] neg_items_emb = self.item_embedding(neg_items) # [B H] pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] loss = self.loss_fct(pos_score, neg_score) return loss else: # self.loss_type = 'CE' test_item_emb = self.item_embedding.weight logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) loss = self.loss_fct(logits, pos_items) return loss
[docs] def predict(self, interaction): item_seq = interaction[self.ITEM_SEQ] item_seq_len = interaction[self.ITEM_SEQ_LEN] test_item = interaction[self.ITEM_ID] seq_output = self.forward(item_seq, item_seq_len) test_item_emb = self.item_embedding(test_item) scores = torch.mul(seq_output, test_item_emb).sum(dim=1) # [B] return scores
[docs] def full_sort_predict(self, interaction): item_seq = interaction[self.ITEM_SEQ] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(item_seq, item_seq_len) test_items_emb = self.item_embedding.weight scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B n_items] return scores