本文的核心思想是是将Attention mechanism 用到FM的second-order relations的建模当中，这个过程理所当然的用到了NN技术。
具体做法：
为每个特征之间的关系建立一个向量v_ij， 和v_ji; 用这两个向量替换FM中的v_i 和 v_j; 这样<v_ij, v_ji>就有了新的表达，继而采用向量变换得到想要规模的新向量（最后的形式： 为每一个特征都构造了一个n维的向量，其中每一个维度代表当前特征和其他特征之间的联系）。
将Attention Mechanism用在上述向量当中，得到每一个维度的权重，即attention，最后再把attention分别乘到上述向量当中（wise-product）所谓的attention越大，该两个feature之间的second-order就越重要。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine | |||||||||||
| 文献作者 | JunlinZhang | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| attention; second-order; FFM | |||||||||||
| 摘要描述 | |||||||||||
| Click through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems. Recent years have witnessed the success of both the deep learning-based model and attention mechanism in various tasks in computer vision (CV) and natural language processing (NLP). How to combine the attention mechanism with deep CTR model is a promising direction because it may ensemble the advantages of both sides. Although some CTR model such as Attentional Factorization Machine (AFM) has been proposed to model the weight of second order interaction features, we posit the evaluation of feature importance before explicit feature interaction procedure is also important for CTR prediction tasks because the model can learn to selectively highlight the informative features and suppress less useful ones if the task has many input features. In this paper, we propose a new neural CTR model named Field Attentive Deep Field- aware Factorization Machine (FAT-DeepFFM) by combining the Deep Field-aware Factorization Machine (DeepFFM) with Compose-Excitation network (CENet) field attention mechanism which is proposed by us as an enhanced version of Squeeze- Excitation network (SENet) to highlight the feature importance. We conduct extensive experiments on two real-world datasets and the experiment results show that FAT-DeepFFM achieves the best perfor- mance and obtains different improvements over the state-of-the-art methods. We also compare two kinds of attention mechanisms (attention before ex- plicit feature interaction vs. attention after explicit feature interaction) and demonstrate that the former one outperforms the latter one significantly. | |||||||||||
