推荐系统 |学习笔记：DeepFM

论文地址

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

推荐系统遇上深度学习(三)–DeepFM模型理论和实践

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Abstract

• Despite great progress, existing
  methods seem to have a strong bias towards
  low- or high-order interactions, or require expertise
  feature engineering.
• The proposed model,
  DeepFM, combines the power of factorization machines
  for recommendation and deep learning for
  feature learning in a new neural network architecture.

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1 Introduction

• a linear model lacks the ability
  to learn feature interactions, and a common practice is
  to manually include pairwise feature interactions in its feature
  vector. Such a method is hard to generalize to model
  high-order feature interactions or those never or rarely appear
  in the training data
• While in principle FM can model
  high-order feature interaction, in practice usually only order-
  2 feature interactions are considered due to high complexity.
• CNN-based models are biased to the interactions
  between neighboring features while RNN-based
  models are more suitable for click data with sequential dependency.
• (FNN). This model pre-trains FM before applying
  DNN, thus limited by the capability of FM.
• PNN and FNN, like other deep
  models, capture little low-order feature interactions, which
  are also essential for CTR prediction.
• In
  this paper, we show it is possible to derive a learning model