# -*- coding: utf-8 -*-
# @Time : 2020/11/22 14:56
# @Author : Shao Weiqi
# @Reviewer : Lin Kun
# @Email : shaoweiqi@ruc.edu.cn
r"""
NPE
################################################
Reference:
ThaiBinh Nguyen, et al. "NPE: Neural Personalized Embedding for Collaborative Filtering" in IJCAI 2018.
Reference code:
https://github.com/wubinzzu/NeuRec
"""
import torch
import torch.nn as nn
from torch.nn.init import xavier_normal_
from recbole.model.abstract_recommender import SequentialRecommender
from recbole.model.loss import BPRLoss
[docs]class NPE(SequentialRecommender):
r"""
models a user’s click to an item in two terms: the personal preference of the user for the item,
and the relationships between this item and other items clicked by the user
"""
def __init__(self, config, dataset):
super(NPE, self).__init__(config, dataset)
# load the dataset information
self.n_user = dataset.num(self.USER_ID)
self.device = config["device"]
# load the parameters information
self.embedding_size = config["embedding_size"]
self.dropout_prob = config["dropout_prob"]
# define layers and loss type
self.user_embedding = nn.Embedding(self.n_user, self.embedding_size)
self.item_embedding = nn.Embedding(self.n_items, self.embedding_size)
self.embedding_seq_item = nn.Embedding(
self.n_items, self.embedding_size, padding_idx=0
)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(self.dropout_prob)
self.loss_type = config["loss_type"]
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']!")
# init the parameters of the module
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Embedding):
xavier_normal_(module.weight.data)
[docs] def forward(self, seq_item, user):
user_embedding = self.dropout(self.relu(self.user_embedding(user)))
# batch_size * embedding_size
seq_item_embedding = self.item_embedding(seq_item).sum(dim=1)
seq_item_embedding = self.dropout(self.relu(seq_item_embedding))
# batch_size * embedding_size
return user_embedding + seq_item_embedding
[docs] def calculate_loss(self, interaction):
seq_item = interaction[self.ITEM_SEQ]
user = interaction[self.USER_ID]
seq_output = self.forward(seq_item, user)
pos_items = interaction[self.POS_ITEM_ID]
pos_items_embs = self.item_embedding(pos_items)
if self.loss_type == "BPR":
neg_items = interaction[self.NEG_ITEM_ID]
neg_items_emb = self.relu(self.item_embedding(neg_items))
pos_items_emb = self.relu(pos_items_embs)
pos_score = torch.sum(seq_output * pos_items_emb, dim=-1)
neg_score = torch.sum(seq_output * neg_items_emb, dim=-1)
loss = self.loss_fct(pos_score, neg_score)
return loss
else: # self.loss_type = 'CE'
test_item_emb = self.relu(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]
test_item = interaction[self.ITEM_ID]
user = interaction[self.USER_ID]
seq_output = self.forward(item_seq, user)
test_item_emb = self.relu(self.item_embedding(test_item))
scores = torch.mul(seq_output, test_item_emb).sum(dim=1)
return scores
[docs] def full_sort_predict(self, interaction):
item_seq = interaction[self.ITEM_SEQ]
user = interaction[self.USER_ID]
seq_output = self.forward(item_seq, user)
test_items_emb = self.relu(self.item_embedding.weight)
scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1))
return scores