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ssl4rec.py
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r"""
SSL4REC
################################################
Reference:
Tiansheng Yao et al. "Self-supervised Learning for Large-scale Item Recommendations." in CIKM 2021.
Reference code:
https://github.com/Coder-Yu/SELFRec/model/graph/SSL4Rec.py
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from recbole.model.loss import EmbLoss
from recbole.utils import InputType
from recbole.model.init import xavier_uniform_initialization
from recbole_gnn.model.abstract_recommender import GeneralGraphRecommender
class SSL4REC(GeneralGraphRecommender):
input_type = InputType.PAIRWISE
def __init__(self, config, dataset):
super(SSL4REC, self).__init__(config, dataset)
# load parameters info
self.tau = config["tau"]
self.reg_weight = config["reg_weight"]
self.cl_rate = config["ssl_weight"]
self.require_pow = config["require_pow"]
self.reg_loss = EmbLoss()
self.encoder = DNN_Encoder(config, dataset)
# storage variables for full sort evaluation acceleration
self.restore_user_e = None
self.restore_item_e = None
# parameters initialization
self.apply(xavier_uniform_initialization)
self.other_parameter_name = ['restore_user_e', 'restore_item_e']
def forward(self, user, item):
user_e, item_e = self.encoder(user, item)
return user_e, item_e
def calculate_batch_softmax_loss(self, user_emb, item_emb, temperature):
user_emb, item_emb = F.normalize(user_emb, dim=1), F.normalize(item_emb, dim=1)
pos_score = (user_emb * item_emb).sum(dim=-1)
pos_score = torch.exp(pos_score / temperature)
ttl_score = torch.matmul(user_emb, item_emb.transpose(0, 1))
ttl_score = torch.exp(ttl_score / temperature).sum(dim=1)
loss = -torch.log(pos_score / ttl_score + 10e-6)
return torch.mean(loss)
def calculate_loss(self, interaction):
# clear the storage variable when training
if self.restore_user_e is not None or self.restore_item_e is not None:
self.restore_user_e, self.restore_item_e = None, None
user = interaction[self.USER_ID]
pos_item = interaction[self.ITEM_ID]
user_embeddings, item_embeddings = self.forward(user, pos_item)
rec_loss = self.calculate_batch_softmax_loss(user_embeddings, item_embeddings, self.tau)
cl_loss = self.encoder.calculate_cl_loss(pos_item)
reg_loss = self.reg_loss(user_embeddings, item_embeddings, require_pow=self.require_pow)
loss = rec_loss + self.cl_rate * cl_loss + self.reg_weight * reg_loss
return loss
def predict(self, interaction):
user = interaction[self.USER_ID]
item = interaction[self.ITEM_ID]
user_embeddings, item_embeddings = self.forward(user, item)
u_embeddings = user_embeddings[user]
i_embeddings = item_embeddings[item]
scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1)
return scores
def full_sort_predict(self, interaction):
user = interaction[self.USER_ID]
if self.restore_user_e is None or self.restore_item_e is None:
self.restore_user_e, self.restore_item_e = self.forward(torch.arange(
self.n_users, device=self.device), torch.arange(self.n_items, device=self.device))
# get user embedding from storage variable
u_embeddings = self.restore_user_e[user]
# dot with all item embedding to accelerate
scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1))
return scores.view(-1)
class DNN_Encoder(nn.Module):
def __init__(self, config, dataset):
super(DNN_Encoder, self).__init__()
self.emb_size = config["embedding_size"]
self.drop_ratio = config["drop_ratio"]
self.tau = config["tau"]
self.USER_ID = config["USER_ID_FIELD"]
self.ITEM_ID = config["ITEM_ID_FIELD"]
self.n_users = dataset.num(self.USER_ID)
self.n_items = dataset.num(self.ITEM_ID)
self.user_tower = nn.Sequential(
nn.Linear(self.emb_size, 1024),
nn.ReLU(True),
nn.Linear(1024, 128),
nn.Tanh()
)
self.item_tower = nn.Sequential(
nn.Linear(self.emb_size, 1024),
nn.ReLU(True),
nn.Linear(1024, 128),
nn.Tanh()
)
self.dropout = nn.Dropout(self.drop_ratio)
self.initial_user_emb = nn.Embedding(self.n_users, self.emb_size)
self.initial_item_emb = nn.Embedding(self.n_items, self.emb_size)
self.reset_parameters()
def reset_parameters(self):
nn.init.xavier_uniform_(self.initial_user_emb.weight)
nn.init.xavier_uniform_(self.initial_item_emb.weight)
def forward(self, q, x):
q_emb = self.initial_user_emb(q)
i_emb = self.initial_item_emb(x)
q_emb = self.user_tower(q_emb)
i_emb = self.item_tower(i_emb)
return q_emb, i_emb
def item_encoding(self, x):
i_emb = self.initial_item_emb(x)
i1_emb = self.dropout(i_emb)
i2_emb = self.dropout(i_emb)
i1_emb = self.item_tower(i1_emb)
i2_emb = self.item_tower(i2_emb)
return i1_emb, i2_emb
def calculate_cl_loss(self, idx):
x1, x2 = self.item_encoding(idx)
x1, x2 = F.normalize(x1, dim=-1), F.normalize(x2, dim=-1)
pos_score = (x1 * x2).sum(dim=-1)
pos_score = torch.exp(pos_score / self.tau)
ttl_score = torch.matmul(x1, x2.transpose(0, 1))
ttl_score = torch.exp(ttl_score / self.tau).sum(dim=1)
return -torch.log(pos_score / ttl_score).mean()