# -*- encoding: utf-8 -*-
# @Time : 2020/09/28
# @Author : Kaiyuan Li
# @email : tsotfsk@outlook.com
"""
FISM
#######################################
Reference:
S. Kabbur et al. "FISM: Factored item similarity models for top-n recommender systems" in KDD 2013
Reference code:
https://github.com/AaronHeee/Neural-Attentive-Item-Similarity-Model
"""
import torch
import torch.nn as nn
from torch.nn.init import normal_
from recbole.model.abstract_recommender import GeneralRecommender
from recbole.utils import InputType
[docs]class FISM(GeneralRecommender):
"""FISM is an item-based model for generating top-N recommendations that learns the
item-item similarity matrix as the product of two low dimensional latent factor matrices.
These matrices are learned using a structural equation modeling approach, where in the
value being estimated is not used for its own estimation.
"""
input_type = InputType.POINTWISE
def __init__(self, config, dataset):
super(FISM, self).__init__(config, dataset)
# load dataset info
self.LABEL = config['LABEL_FIELD']
# get all users' history interaction information.the history item
# matrix is padding by the maximum number of a user's interactions
self.history_item_matrix, self.history_lens, self.mask_mat = self.get_history_info(dataset)
# load parameters info
self.embedding_size = config['embedding_size']
self.reg_weights = config['reg_weights']
self.alpha = config['alpha']
self.split_to = config['split_to']
# split the too large dataset into the specified pieces
if self.split_to > 0:
self.group = torch.chunk(torch.arange(self.n_items).to(self.device), self.split_to)
else:
self.logger.warning('Pay Attetion!! the `split_to` is set to 0. If you catch a OMM error in this case, ' + \
'you need to increase it \n\t\t\tuntil the error disappears. For example, ' + \
'you can append it in the command line such as `--split_to=5`')
# define layers and loss
# construct source and destination item embedding matrix
self.item_src_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0)
self.item_dst_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0)
self.user_bias = nn.Parameter(torch.zeros(self.n_users))
self.item_bias = nn.Parameter(torch.zeros(self.n_items))
self.bceloss = nn.BCELoss()
# parameters initialization
self.apply(self._init_weights)
[docs] def get_history_info(self, dataset):
"""get the user history interaction information
Args:
dataset (DataSet): train dataset
Returns:
tuple: (history_item_matrix, history_lens, mask_mat)
"""
history_item_matrix, _, history_lens = dataset.history_item_matrix()
history_item_matrix = history_item_matrix.to(self.device)
history_lens = history_lens.to(self.device)
arange_tensor = torch.arange(history_item_matrix.shape[1]).to(self.device)
mask_mat = (arange_tensor < history_lens.unsqueeze(1)).float()
return history_item_matrix, history_lens, mask_mat
[docs] def reg_loss(self):
"""calculate the reg loss for embedding layers
Returns:
torch.Tensor: reg loss
"""
reg_1, reg_2 = self.reg_weights
loss_1 = reg_1 * self.item_src_embedding.weight.norm(2)
loss_2 = reg_2 * self.item_dst_embedding.weight.norm(2)
return loss_1 + loss_2
def _init_weights(self, module):
"""Initialize the module's parameters
Note:
It's a little different from the source code, because pytorch has no function to initialize
the parameters by truncated normal distribution, so we replace it with xavier normal distribution
"""
if isinstance(module, nn.Embedding):
normal_(module.weight.data, 0, 0.01)
[docs] def inter_forward(self, user, item):
"""forward the model by interaction
"""
user_inter = self.history_item_matrix[user]
item_num = self.history_lens[user].unsqueeze(1)
batch_mask_mat = self.mask_mat[user]
user_history = self.item_src_embedding(user_inter) # batch_size x max_len x embedding_size
target = self.item_dst_embedding(item) # batch_size x embedding_size
user_bias = self.user_bias[user] # batch_size x 1
item_bias = self.item_bias[item]
similarity = torch.bmm(user_history, target.unsqueeze(2)).squeeze(2) # batch_size x max_len
similarity = batch_mask_mat * similarity
coeff = torch.pow(item_num.squeeze(1), -self.alpha)
scores = torch.sigmoid(coeff.float() * torch.sum(similarity, dim=1) + user_bias + item_bias)
return scores
[docs] def user_forward(self, user_input, item_num, user_bias, repeats=None, pred_slc=None):
"""forward the model by user
Args:
user_input (torch.Tensor): user input tensor
item_num (torch.Tensor): user history interaction lens
repeats (int, optional): the number of items to be evaluated
pred_slc (torch.Tensor, optional): continuous index which controls the current evaluation items,
if pred_slc is None, it will evaluate all items
Returns:
torch.Tensor: result
"""
item_num = item_num.repeat(repeats, 1)
user_history = self.item_src_embedding(user_input) # inter_num x embedding_size
user_history = user_history.repeat(repeats, 1, 1) # target_items x inter_num x embedding_size
if pred_slc is None:
targets = self.item_dst_embedding.weight # target_items x embedding_size
item_bias = self.item_bias
else:
targets = self.item_dst_embedding(pred_slc)
item_bias = self.item_bias[pred_slc]
similarity = torch.bmm(user_history, targets.unsqueeze(2)).squeeze(2) # inter_num x target_items
coeff = torch.pow(item_num.squeeze(1), -self.alpha)
scores = torch.sigmoid(coeff.float() * torch.sum(similarity, dim=1) + user_bias + item_bias)
return scores
[docs] def forward(self, user, item):
return self.inter_forward(user, item)
[docs] def calculate_loss(self, interaction):
user = interaction[self.USER_ID]
item = interaction[self.ITEM_ID]
label = interaction[self.LABEL]
output = self.forward(user, item)
loss = self.bceloss(output, label) + self.reg_loss()
return loss
[docs] def full_sort_predict(self, interaction):
user = interaction[self.USER_ID]
batch_user_bias = self.user_bias[user]
user_inters = self.history_item_matrix[user]
item_nums = self.history_lens[user]
scores = []
# test users one by one, if the number of items is too large, we will split it to some pieces
for user_input, item_num, user_bias in zip(user_inters, item_nums.unsqueeze(1), batch_user_bias):
if self.split_to <= 0:
output = self.user_forward(user_input[:item_num], item_num, user_bias, repeats=self.n_items)
else:
output = []
for mask in self.group:
tmp_output = self.user_forward(
user_input[:item_num], item_num, user_bias, repeats=len(mask), pred_slc=mask
)
output.append(tmp_output)
output = torch.cat(output, dim=0)
scores.append(output)
result = torch.cat(scores, dim=0)
return result
[docs] def predict(self, interaction):
user = interaction[self.USER_ID]
item = interaction[self.ITEM_ID]
output = self.forward(user, item)
return output