Source code for recbole.model.context_aware_recommender.afm

# -*- coding: utf-8 -*-
# @Time   : 2020/7/21
# @Author : Zihan Lin
# @Email  : linzihan.super@foxmail.com
# @File   : afm.py

r"""
AFM
################################################
Reference:
    Jun Xiao et al. "Attentional Factorization Machines: Learning the Weight of Feature Interactions via
    Attention Networks" in IJCAI 2017.
"""

import torch
import torch.nn as nn
from torch.nn.init import xavier_normal_, constant_

from recbole.model.abstract_recommender import ContextRecommender
from recbole.model.layers import AttLayer


[docs]class AFM(ContextRecommender): """ AFM is a attention based FM model that predict the final score with the attention of input feature. """ def __init__(self, config, dataset): super(AFM, self).__init__(config, dataset) # load parameters info self.attention_size = config['attention_size'] self.dropout_prob = config['dropout_prob'] self.reg_weight = config['reg_weight'] self.num_pair = self.num_feature_field * (self.num_feature_field - 1) / 2 # define layers and loss self.attlayer = AttLayer(self.embedding_size, self.attention_size) self.p = nn.Parameter(torch.randn(self.embedding_size), requires_grad=True) self.dropout_layer = nn.Dropout(p=self.dropout_prob) self.sigmoid = nn.Sigmoid() self.loss = nn.BCELoss() # parameters initialization self.apply(self._init_weights) def _init_weights(self, module): if isinstance(module, nn.Embedding): xavier_normal_(module.weight.data) elif isinstance(module, nn.Linear): xavier_normal_(module.weight.data) if module.bias is not None: constant_(module.bias.data, 0)
[docs] def build_cross(self, feat_emb): """ Build the cross feature columns of feature columns Args: feat_emb (torch.FloatTensor): input feature embedding tensor. shape of [batch_size, field_size, embed_dim]. Returns: tuple: - torch.FloatTensor: Left part of the cross feature. shape of [batch_size, num_pairs, emb_dim]. - torch.FloatTensor: Right part of the cross feature. shape of [batch_size, num_pairs, emb_dim]. """ # num_pairs = num_feature_field * (num_feature_field-1) / 2 row = [] col = [] for i in range(self.num_feature_field - 1): for j in range(i + 1, self.num_feature_field): row.append(i) col.append(j) p = feat_emb[:, row] # [batch_size, num_pairs, emb_dim] q = feat_emb[:, col] # [batch_size, num_pairs, emb_dim] return p, q
[docs] def afm_layer(self, infeature): """ Get the attention-based feature interaction score Args: infeature (torch.FloatTensor): input feature embedding tensor. shape of [batch_size, field_size, embed_dim]. Returns: torch.FloatTensor: Result of score. shape of [batch_size, 1]. """ p, q = self.build_cross(infeature) pair_wise_inter = torch.mul(p, q) # [batch_size, num_pairs, emb_dim] # [batch_size, num_pairs, 1] att_signal = self.attlayer(pair_wise_inter).unsqueeze(dim=2) att_inter = torch.mul(att_signal, pair_wise_inter) # [batch_size, num_pairs, emb_dim] att_pooling = torch.sum(att_inter, dim=1) # [batch_size, emb_dim] att_pooling = self.dropout_layer(att_pooling) # [batch_size, emb_dim] att_pooling = torch.mul(att_pooling, self.p) # [batch_size, emb_dim] att_pooling = torch.sum(att_pooling, dim=1, keepdim=True) # [batch_size, 1] return att_pooling
[docs] def forward(self, interaction): afm_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] output = self.sigmoid(self.first_order_linear(interaction) + self.afm_layer(afm_all_embeddings)) return output.squeeze(-1)
[docs] def calculate_loss(self, interaction): label = interaction[self.LABEL] output = self.forward(interaction) l2_loss = self.reg_weight * torch.norm(self.attlayer.w.weight, p=2) return self.loss(output, label) + l2_loss
[docs] def predict(self, interaction): return self.forward(interaction)