Source code for recbole.model.context_aware_recommender.fnn

# -*- coding: utf-8 -*-
# @Time   : 2020/9/15 10:57
# @Author : Zihan Lin
# @Email  : linzihan.super@foxmail.com
# @File   : fnn.py

r"""
FNN
################################################
Reference:
    Weinan Zhang1 et al. "Deep Learning over Multi-field Categorical Data" in ECIR 2016
"""

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 MLPLayers


[docs]class FNN(ContextRecommender): """FNN which also called DNN is a basic version of CTR model that use mlp from field features to predict score. Note: Based on the experiments in the paper above, This implementation incorporate Dropout instead of L2 normalization to relieve over-fitting. Our implementation of FNN is a basic version without pretrain support. If you want to pretrain the feature embedding as the original paper, we suggest you to construct a advanced FNN model and train it in two-stage process with our FM model. """ def __init__(self, config, dataset): super(FNN, self).__init__(config, dataset) # load parameters info self.mlp_hidden_size = config['mlp_hidden_size'] self.dropout_prob = config['dropout_prob'] size_list = [self.embedding_size * self.num_feature_field] + self.mlp_hidden_size # define layers and loss self.mlp_layers = MLPLayers(size_list, self.dropout_prob, activation='tanh', bn=False) # use tanh as activation self.predict_layer = nn.Linear(self.mlp_hidden_size[-1], 1, bias=True) 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 forward(self, interaction): fnn_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] batch_size = fnn_all_embeddings.shape[0] output = self.predict_layer(self.mlp_layers(fnn_all_embeddings.view(batch_size, -1))) output = self.sigmoid(output) return output.squeeze(-1)
[docs] def calculate_loss(self, interaction): label = interaction[self.LABEL] output = self.forward(interaction) return self.loss(output, label)
[docs] def predict(self, interaction): return self.forward(interaction)