DeepFM: A Factorization-Machine based Neural Network for CTR Prediction【论文记录】

1 摘要

• DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning
  DeepFM 结合了因子分解机的推荐能力和特征学习的深度学习功能

2 介绍

• CNN-based models are biased to the interactions between neighboring features while RNN-based models are more suitable for click data with sequential dependency
  基于 CNN 的模型偏向于相邻特征之间的交互，而基于 RNN 的模型更适合于具有顺序依赖的点击数据

• PNN and FNN, like other deep models, capture little low-order feature interactions
  PNN、FNN 与其他深层模型一样，捕获的低阶特征交互很少

• DeepFM can be trained efficiently because its wide part and deep part, share the same input and also the embedding vector.
  DeepFM 的宽组件和深组件共享同一输入和嵌入向量，因此可以有效地训练 DeepFM。

3 方法

• FM component and deep component, that share the same input
  FM 分量和 deep 分量，它们共享相同的输入

3.1 FM部分

• In previous approaches, the parameter of an interaction of features $i$ and $j$ can be trained only when feature $i$ and feature $j$ both appear in the same data record.

  While in FM, it is measured via the inner product of their latent vectors $V_i$ and $V_j$. Thanks to this flexible design, FM can train latent vector $V_i$