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import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.helper import get_norm_adj_mat, ssl_loss, topk_sample, cal_diff_loss, propgt_info
class REARM(nn.Module):
def __init__(self, config, dataset):
super(REARM, self).__init__()
self.n_users = dataset.n_users
self.n_items = dataset.n_items
self.n_nodes = self.n_users + self.n_items
self.i_v_feat = dataset.i_v_feat
self.i_t_feat = dataset.i_t_feat
self.embedding_dim = config.embedding_dim
self.feat_embed_dim = config.embedding_dim
self.dim_feat = self.feat_embed_dim
self.reg_weight = config.reg_weight
self.device = config.device
self.cl_tmp = config.cl_tmp
self.cl_loss_weight = config.cl_loss_weight
self.diff_loss_weight = config.diff_loss_weight
self.n_layers = config.n_layers
self.num_user_co = config.num_user_co
self.num_item_co = config.num_item_co
self.user_aggr_mode = config.user_aggr_mode
self.n_ii_layers = config.n_ii_layers
self.n_uu_layers = config.n_uu_layers
self.k = config.rank
self.uu_co_weight = config.uu_co_weight
self.ii_co_weight = config.ii_co_weight
# Load user and item graphs
self.topK_users = dataset.topK_users
self.topK_items = dataset.topK_items
self.dict_user_co_occ_graph = dataset.dict_user_co_occ_graph
self.dict_item_co_occ_graph = dataset.dict_item_co_occ_graph
self.topK_users_counts = dataset.topK_users_counts
self.topK_items_counts = dataset.topK_items_counts
self.s_drop = config.s_drop
self.m_drop = config.m_drop
self.ly_norm = nn.LayerNorm(self.feat_embed_dim)
self.self_i_attn1 = nn.MultiheadAttention(1, 1, dropout=self.s_drop, batch_first=True)
self.self_i_attn2 = nn.MultiheadAttention(1, 1, dropout=self.s_drop, batch_first=True)
self.mutual_i_attn1 = nn.MultiheadAttention(1, 1, dropout=self.m_drop, batch_first=True)
self.mutual_i_attn2 = nn.MultiheadAttention(1, 1, dropout=self.m_drop, batch_first=True)
self.user_id_embedding = nn.Embedding(self.n_users, self.embedding_dim).to(self.device)
self.item_id_embedding = nn.Embedding(self.n_items, self.embedding_dim).to(self.device)
self.prl = nn.PReLU().to(self.device)
self.cal_bpr = torch.tensor([[1.0], [-1.0]]).to(self.device)
# load dataset info
self.norm_adj = get_norm_adj_mat(self, dataset.sparse_inter_matrix(form='coo')).to(self.device)
# Process to obtain user co-occurrence matrix (n_users*num_user_co)
self.user_co_graph = topk_sample(self.n_users, self.dict_user_co_occ_graph, self.num_user_co,
self.topK_users, self.topK_users_counts, 'softmax',
self.device)
# Process to obtain user co-occurrence matrix (n_users*num_user_co)
self.item_co_graph = topk_sample(self.n_items, self.dict_item_co_occ_graph, self.num_item_co,
self.topK_items, self.topK_items_counts, 'softmax',
self.device)
# Process to obtain item similarity matrix (n_items* n_items )
self.i_mm_adj = dataset.i_mm_adj
# Process to obtain user similarity matrix (n_users* n_users)
self.u_mm_adj = dataset.u_mm_adj
# Strengthen ii and uu graphs
self.stre_ii_graph = self.ii_co_weight * self.item_co_graph + (1.0 - self.ii_co_weight) * self.i_mm_adj
self.stre_uu_graph = self.uu_co_weight * self.user_co_graph + (1.0 - self.uu_co_weight) * self.u_mm_adj
if self.i_v_feat is not None:
self.image_embedding = nn.Embedding.from_pretrained(self.i_v_feat, freeze=False).to(self.device)
self.image_i_trs = nn.Linear(self.i_v_feat.shape[1], self.feat_embed_dim)
self.user_v_prefer = torch.nn.Parameter(dataset.u_v_interest, requires_grad=True).to(self.device)
self.image_u_trs = nn.Linear(self.i_v_feat.shape[1], self.feat_embed_dim)
if self.i_t_feat is not None:
self.text_embedding = nn.Embedding.from_pretrained(self.i_t_feat, freeze=False).to(self.device)
self.text_i_trs = nn.Linear(self.i_t_feat.shape[1], self.feat_embed_dim)
self.user_t_prefer = torch.nn.Parameter(dataset.u_t_interest, requires_grad=True).to(self.device)
self.text_u_trs = nn.Linear(self.i_t_feat.shape[1], self.feat_embed_dim)
# MLP(input_dim, feature_dim, hidden_dim, output_dim)
self.mlp_u1 = MLP(self.feat_embed_dim, self.feat_embed_dim * self.k, self.feat_embed_dim * self.k, self.device)
self.mlp_u2 = MLP(self.feat_embed_dim, self.feat_embed_dim * self.k, self.feat_embed_dim * self.k, self.device)
self.mlp_i1 = MLP(self.feat_embed_dim, self.feat_embed_dim * self.k, self.feat_embed_dim * self.k, self.device)
self.mlp_i2 = MLP(self.feat_embed_dim, self.feat_embed_dim * self.k, self.feat_embed_dim * self.k, self.device)
self.meta_netu = nn.Linear(self.feat_embed_dim * 2, self.feat_embed_dim, bias=True) # Knowledge compression
self.meta_neti = nn.Linear(self.feat_embed_dim * 2, self.feat_embed_dim, bias=True) # Knowledge compression
self._reset_parameters()
def _reset_parameters(self):
nn.init.normal_(self.user_id_embedding.weight, std=0.1)
nn.init.normal_(self.item_id_embedding.weight, std=0.1)
nn.init.xavier_normal_(self.image_i_trs.weight)
nn.init.xavier_normal_(self.text_i_trs.weight)
nn.init.xavier_normal_(self.image_u_trs.weight)
nn.init.xavier_normal_(self.text_u_trs.weight)
def forward(self):
# Uniform feature dimensions for multi-modal feature information on item
trs_item_v_feat = self.image_i_trs(self.image_embedding.weight)
trs_item_t_feat = self.text_i_trs(self.text_embedding.weight) # num_items * 64
trs_user_v_prefer = self.image_u_trs(self.user_v_prefer)
trs_user_t_prefer = self.text_u_trs(self.user_t_prefer) # num_items * 64
# ====================================================================================
# Homography Relation Learning
# ====================================================================================
# Item homogeneous relational learning
item_v_t = torch.cat((trs_item_v_feat, trs_item_t_feat), dim=-1)
item_id_v_t = torch.cat((self.item_id_embedding.weight, item_v_t), dim=-1)
item_id_v_t = propgt_info(item_id_v_t, self.n_ii_layers, self.stre_ii_graph, last_layer=True)
item_id_v_t = F.normalize(item_id_v_t)
item_id_ii = item_id_v_t[:, :self.embedding_dim]
gnn_i_v_feat = item_id_v_t[:, self.feat_embed_dim:-self.feat_embed_dim]
gnn_i_t_feat = item_id_v_t[:, -self.feat_embed_dim:]
# User homogeneous relational learning
user_v_t = torch.cat((trs_user_v_prefer, trs_user_t_prefer), dim=-1)
user_id_v_t = torch.cat((self.user_id_embedding.weight, user_v_t), dim=-1)
user_id_v_t = propgt_info(user_id_v_t, self.n_uu_layers, self.stre_uu_graph, last_layer=True)
user_id_v_t = F.normalize(user_id_v_t)
user_id_uu = user_id_v_t[:, :self.embedding_dim]
gnn_u_v_prefer = user_id_v_t[:, self.embedding_dim:-self.feat_embed_dim]
gnn_u_t_prefer = user_id_v_t[:, -self.feat_embed_dim:]
# ====================================================================================
# Item Feature Attention Integration
# ====================================================================================
# Item visual features self-attention
item_v_feat, _ = self.self_i_attn1(gnn_i_v_feat.unsqueeze(2), gnn_i_v_feat.unsqueeze(2),
gnn_i_v_feat.unsqueeze(2), need_weights=False)
item_v_feat = self.ly_norm(gnn_i_v_feat + item_v_feat.squeeze())
item_v_feat = self.prl(item_v_feat)
# Item text features self-attention
item_t_feat, _ = self.self_i_attn2(gnn_i_t_feat.unsqueeze(2), gnn_i_t_feat.unsqueeze(2),
gnn_i_t_feat.unsqueeze(2), need_weights=False)
item_t_feat = self.ly_norm(gnn_i_t_feat + item_t_feat.squeeze())
item_t_feat = self.prl(item_t_feat)
# ---------------------------------------------------------------------------------------
# Item text to visual cross-attention
i_t2v_feat, _ = self.mutual_i_attn1(item_t_feat.unsqueeze(2), item_v_feat.unsqueeze(2),
item_v_feat.unsqueeze(2), need_weights=False)
item_t2v_feat = self.ly_norm(item_v_feat + i_t2v_feat.squeeze())
item_t2v_feat = self.prl(item_t2v_feat)
# Item visual to text cross-attention
i_v2t_feat, _ = self.mutual_i_attn2(item_v_feat.unsqueeze(2), item_t_feat.unsqueeze(2),
item_t_feat.unsqueeze(2), need_weights=False)
item_v2t_feat = self.ly_norm(item_t_feat.squeeze() + i_v2t_feat.squeeze())
item_v2t_feat = self.prl(item_v2t_feat)
user_v_prefer = self.prl(gnn_u_v_prefer) # (num_items* 64)
user_t_prefer = self.prl(gnn_u_t_prefer)
# ====================================================================================
# Heterography Relation Learning
# ====================================================================================
# Item feature splicing with total attentions
item_v_t_feat = torch.cat((item_t2v_feat, item_v2t_feat), dim=-1) # (num_items* 128)
user_v_t_prefer = torch.cat((user_v_prefer, user_t_prefer), dim=-1) # (num_user* 128)
ego_feat_prefer = torch.cat((user_v_t_prefer, item_v_t_feat), dim=0) # (num_users+num_items）* 128)
self.fin_feat_prefer = propgt_info(ego_feat_prefer, self.n_layers, self.norm_adj)
ego_id_embed = torch.cat((user_id_uu, item_id_ii), dim=0) # (num_users+num_items）* 64)
fin_id_embed = propgt_info(ego_id_embed, self.n_layers, self.norm_adj)
share_knowldge = self.meta_extra_share(fin_id_embed, self.fin_feat_prefer) # (num_users+num_items）* 64)
fin_v = self.prl(self.fin_feat_prefer[:, :self.embedding_dim]) + fin_id_embed
fin_t = self.prl(self.fin_feat_prefer[:, self.embedding_dim:]) + fin_id_embed
fin_share = self.prl(share_knowldge) + fin_id_embed
temp_full_feat_prefer = torch.cat((fin_v, fin_t), dim=-1)
representation = torch.cat((temp_full_feat_prefer, fin_share), dim=-1)
return representation
def loss(self, user_tensor, item_tensor):
user_tensor_flatten = user_tensor.view(-1)
item_tensor_flatten = item_tensor.view(-1)
out = self.forward()
user_rep = out[user_tensor_flatten]
item_rep = out[item_tensor_flatten]
score = torch.sum(user_rep * item_rep, dim=1).view(-1, 2)
bpr_score = torch.matmul(score, self.cal_bpr)
bpr_loss = -torch.mean(nn.LogSigmoid()(bpr_score))
# Loss of multi-modal feature contrasts
i_mul_vt_cl_loss = ssl_loss(self.fin_feat_prefer[:, :self.feat_embed_dim],
self.fin_feat_prefer[:, -self.feat_embed_dim:], item_tensor_flatten, self.cl_tmp)
u_mul_vt_cl_loss = ssl_loss(self.fin_feat_prefer[:, :self.feat_embed_dim],
self.fin_feat_prefer[:, -self.feat_embed_dim:], user_tensor_flatten, self.cl_tmp)
mul_vt_cl_loss = self.cl_loss_weight * (i_mul_vt_cl_loss + u_mul_vt_cl_loss)
# Modal-unique orthogonal constraint
mul_i_diff_loss = cal_diff_loss(self.fin_feat_prefer, user_tensor, self.feat_embed_dim)
mul_u_diff_loss = cal_diff_loss(self.fin_feat_prefer, item_tensor, self.feat_embed_dim)
mul_diff_loss = self.diff_loss_weight * (mul_i_diff_loss + mul_u_diff_loss)
reg_loss = 0 # Realized in AdamW
total_loss = bpr_loss + reg_loss + mul_vt_cl_loss + mul_diff_loss
return total_loss, bpr_loss, reg_loss, mul_vt_cl_loss, mul_diff_loss
def full_sort_predict(self, interaction):
user = interaction[0]
representation = self.forward()
u_reps, i_reps = torch.split(representation, [self.n_users, self.n_items], dim=0)
score_mat_ui = torch.matmul(u_reps[user], i_reps.t())
return score_mat_ui
def meta_extra_share(self, id_embed, prefer_or_feat):
u_id_embed = id_embed[:self.n_users, :]
i_id_embed = id_embed[self.n_users:, :]
u_v_t = prefer_or_feat[:self.n_users, :]
i_v_t = prefer_or_feat[self.n_users:, :]
# meta-knowlege extraction
u_knowldge = self.meta_netu(u_v_t).detach()
i_knowldge = self.meta_neti(i_v_t).detach()
""" Personalized transformation parameter matrix """
# Low rank matrix decomposition
metau1 = self.mlp_u1(u_knowldge).reshape(-1, self.feat_embed_dim, self.k) # N_u*d*k [19445, 64, 3]
metau2 = self.mlp_u2(u_knowldge).reshape(-1, self.k, self.feat_embed_dim) # N_u*k*d [19445, 3, 64]
metai1 = self.mlp_i1(i_knowldge).reshape(-1, self.feat_embed_dim, self.k) # N_i*d*k [7050, 64, 3]
metai2 = self.mlp_i2(i_knowldge).reshape(-1, self.k, self.feat_embed_dim) # N_i*k*d [7050, 3,64]
meta_biasu = torch.mean(metau1, dim=0) # d*k [64, 3]
meta_biasu1 = torch.mean(metau2, dim=0) # k*d [3,64]
meta_biasi = torch.mean(metai1, dim=0) # [64, 3]
meta_biasi1 = torch.mean(metai2, dim=0) # [3, 64]
low_weightu1 = F.softmax(metau1 + meta_biasu, dim=1)
low_weightu2 = F.softmax(metau2 + meta_biasu1, dim=1)
low_weighti1 = F.softmax(metai1 + meta_biasi, dim=1)
low_weighti2 = F.softmax(metai2 + meta_biasi1, dim=1)
# The learned matrix as the weights of the transformed network Equal to a two-layer linear network;
u_middle_knowldge = torch.sum(torch.multiply(u_id_embed.unsqueeze(-1), low_weightu1), dim=1)
u_share_knowldge = torch.sum(torch.multiply(u_middle_knowldge.unsqueeze(-1), low_weightu2), dim=1)
i_middle_knowldge = torch.sum(torch.multiply(i_id_embed.unsqueeze(-1), low_weighti1), dim=1)
i_share_knowldge = torch.sum(torch.multiply(i_middle_knowldge.unsqueeze(-1), low_weighti2), dim=1)
share_knowldge = torch.cat((u_share_knowldge, i_share_knowldge), dim=0)
return share_knowldge
class MLP(torch.nn.Module):
def __init__(self, input_dim, feature_dim, output_dim, device):
super(MLP, self).__init__()
self.device = device
self.linear_pre = nn.Linear(input_dim, feature_dim, bias=True)
self.prl = nn.PReLU().to(self.device)
self.linear_out = nn.Linear(feature_dim, output_dim, bias=True)
def forward(self, data):
x = self.prl(self.linear_pre(data))
x = self.linear_out(x)
x = F.normalize(x, p=2, dim=-1)
return x
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