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216 lines (200 loc) · 8.54 KB
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import torch
import torch.nn as nn
import math
import os
import numpy as np
from torch.nn import functional as F
class Model(nn.Module):
def __init__(self,
user_dim,
item_dim,
num_users,
num_items,
num_feats,
size
):
super(Model, self).__init__()
self.size = size
self.user_dim = user_dim
self.item_dim = item_dim
self.user_item_att = nn.Linear(item_dim+1+num_feats,1)
self.user_user_att = nn.Linear(user_dim,1)
self.item_user_att = nn.Linear(user_dim+1+num_feats,1)
self.item_item_att = nn.Linear(item_dim,1)
self.user_item_emb = nn.Linear(item_dim+1+num_feats,user_dim)
self.item_user_emb = nn.Linear(user_dim+1+num_feats,item_dim)
self.user_item_add = nn.Linear(item_dim+1+num_feats,1)
self.user_user_add = nn.Linear(user_dim,1)
self.item_self_add = nn.Linear(item_dim,1)
self.item_item_add = nn.Linear(item_dim,1)
self.prev_att = nn.Linear(item_dim+num_items,1)
self.prev_wgt = nn.Linear(size,1)
pred_dim = item_dim + num_items
self.pred_emb = nn.Linear(user_dim
+item_dim
+num_users
+num_items,
pred_dim
)
self.pred_itm = nn.Linear(num_items,pred_dim,bias=False)
self.pred_usr = nn.Linear(num_users,num_items,bias=False)
self.pred_lnr = nn.Linear(pred_dim,item_dim+num_items)
self.pred_dyn = nn.Linear(user_dim+item_dim,item_dim)
self.dyna_usr = nn.Linear(user_dim,item_dim)
self.dyna_itm = nn.Linear(item_dim,item_dim,bias=False)
self.pred_stt = nn.Linear(num_users+num_items,num_items)
self.time_lnr = nn.Linear(1, user_dim)
self.pred_itm.weight = nn.Parameter(torch.eye(num_items).cuda()/math.sqrt(num_items))
self.dyna_itm.weight = nn.Parameter(torch.eye(item_dim).cuda()/math.sqrt(item_dim))
def forward(self,
user_embs=None,
item_embs=None,
timediff=None,
mode='user',
freq=None,
user_stat=None,
item_stat=None,
item_max=4,
item_pow=0.75,
user_max=4,
user_pow=0.75
):
if mode == 'user':
item_coe = self.user_item_att(item_embs)
user_coe = self.user_user_att(user_embs)
attn_coe = item_coe * user_coe
neib_emb = self.user_item_emb((torch.exp(attn_coe)*item_embs).sum(0).unsqueeze(0))
output = F.normalize(neib_emb+user_embs)
elif mode == 'item':
item_coe = self.item_item_att(item_embs)
user_coe = self.item_user_att(user_embs)
attn_coe = item_coe * user_coe
neib_emb = self.item_user_emb((torch.exp(attn_coe)*user_embs).sum(0).unsqueeze(0))
output = F.normalize(neib_emb + item_coe*item_embs)
elif mode == 'pred':
freq = freq.clone()
freq[freq>user_max] = user_max
freq /= user_max
freq = (freq**user_pow)*user_max
dyna_usr = self.dyna_usr(F.normalize(freq.unsqueeze(1)*user_embs[:,:self.user_dim]))
dyna_itm = self.dyna_itm(user_embs[:,self.user_dim:])
dyna_pre = dyna_usr + dyna_itm
user_fre = self.pred_usr(user_stat*freq.unsqueeze(1)).detach()
user_slf = self.pred_usr(user_stat)
user_pre = user_fre + user_slf
stat_pre = item_stat + user_pre
output = torch.cat([dyna_pre,stat_pre],dim=1)
elif mode == 'prev':
freq = freq.clone()
freq[freq>item_max] = item_max
freq /= item_max
freq = (freq**item_pow)*item_max
times = np.array([1]*self.size)
times[-1]=2
times = torch.tensor(times).float().cuda()
freq = freq*times
stat_len = freq.sum(1).unsqueeze(1)
stat_len = 1/stat_len
zero_idx = torch.isinf(stat_len)
stat_len[zero_idx] = 1
fre = freq.clone()
fre[torch.nonzero(zero_idx)[:,0],-1] = 1
mask = F.normalize(fre.unsqueeze(2),dim=2)
prev_emb = mask * user_embs
output = F.normalize(prev_emb.mean(1))
elif mode == 'stat':
freq = freq.clone()
freq[freq>item_max] = item_max
freq /= item_max
freq = (freq**item_pow)
times = np.array([1]*self.size)
times[-1]=2
times = torch.tensor(times).float().cuda()
freq = freq* times
stat_len = freq.sum(1).unsqueeze(1)
stat_len = 1/stat_len
zero_idx = torch.isinf(stat_len)
stat_len[zero_idx] = 1
fre = freq.clone()
fre[torch.nonzero(zero_idx)[:,0],-1] = 1
mask = F.normalize(fre.unsqueeze(2),dim=2)
stat_mean = (mask*fre.unsqueeze(2)*item_stat)
stat_pre = 1*self.pred_itm(stat_mean[:,-1,:].squeeze(1))
stat_his = self.pred_itm(stat_mean[:,:-1,:]).sum(1).detach()
output = stat_pre + stat_his
elif mode == 'time':
pass
elif mode == 'addu':
item_coe = self.user_item_add(item_embs)
user_coe = self.user_user_add(user_embs)
attn_coe = item_coe + user_coe
neib_emb = self.user_item_emb((attn_coe*item_embs).sum(0))
output = F.normalize(neib_emb
+ 2*user_coe*user_embs)
elif mode == 'addi':
freq = freq.clone()
freq[freq>item_max] = item_max
freq /= item_max
freq = (freq**item_pow)*item_max
times = np.array([1]*self.size)
times[-1]=2
times = torch.tensor(times).float().cuda()
freq = freq* times
mask = F.normalize(freq.unsqueeze(2),dim=2)
item_coe = self.item_item_att(item_embs).detach()
self_coe = (self.item_item_att(user_embs)).unsqueeze(1)
attn_coe = torch.exp(item_coe * self_coe)
neib_emb = freq.unsqueeze(2)*attn_coe*user_embs.unsqueeze(1)
output = F.normalize(mask*(neib_emb
+ (item_embs)),
dim=2) + (1-mask)*item_embs
return output
class Model2(nn.Module):
def __init__(self,user_dim,item_dim,num_users,num_items,num_feats):
super(Model2,self).__init__()
self.user_rnn = nn.GRUCell(item_dim+1+num_feats,user_dim)
self.item_rnn = nn.GRUCell(user_dim+1+num_feats,item_dim)
self.pred_lnr = nn.Linear(user_dim
+item_dim
+num_users
+num_items,
#+num_feats,
item_dim
+num_items
)
self.time_lnr = nn.Linear(1,user_dim)
def forward(self,user_embs,item_embs=None,time_diff=None,mode='user'):
if mode == 'user':
output = F.normalize(self.user_rnn(item_embs,user_embs))
elif mode == 'item':
output = F.normalize(self.item_rnn(user_embs,item_embs))
elif mode == 'pred':
output = self.pred_lnr(user_embs)
elif mode == 'time':
output = self.time_lnr(time_diff)*user_embs
return output
class ModelNtNs(nn.Module):
def __init__(self,user_dim,item_dim):
super(ModelNtNs,self).__init__()
self.user_rnn = nn.GRUCell(user_dim,user_dim)
self.item_rnn = nn.GRUCell(item_dim,item_dim)
self.pred_lnr = nn.Linear(user_dim+item_dim,
item_dim
)
def forward(self,user_embs,item_embs=None,time_diff=None,mode='user'):
if mode == 'user':
output = self.user_rnn(item_embs,user_embs)
elif mode == 'item':
output = self.item_rnn(user_embs,item_embs)
elif mode == 'pred':
output = self.pred_lnr(user_embs)
return output
if __name__ == '__main__':
model = Model(5,5)
user_embs = torch.rand(1,5)
user_embs = user_embs.repeat(5,1)
item_embs = torch.rand(1,5)
item_embs = item_embs.repeat(5,1)
user_embs = model(user_embs,item_embs,'user')
item_embs = model(user_embs,item_embs,'item')
pass