笔者：杨夕
论文：FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction（结合特征重要性和双线性特征相互作用进行点击率预测）
项目地址：https://github.com/km1994/nlp_paper_study
个人介绍：大佬们好，我叫杨夕，该项目主要是本人在研读顶会论文和复现经典论文过程中，所见、所思、所想、所闻，可能存在一些理解错误，希望大佬们多多指正。
Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo.
- 应用领域
- Among many real-world advertising and feed ranking systems；
- click through rate (CTR) prediction plays a central role.
- 方法：
- logistic regression；
- tree based models；
- factorization machine based models；
- deep learning based CTR models.
However, many current works calculate the feature interactions in a simple way such as Hadamard product and inner product and they care less about the importance of features.
In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions.
- 特点
- the FiBiNET can dynamically learn the importance of features via the Squeeze-Excitation network (SENET) mechanism;
- it is able to effectively learn the feature interactions via bilinear function.
We conduct extensive experiments on two realworld datasets and show that our shallow model outperforms other shallow models such as factorization machine(FM) and field-aware factorization machine(FFM). In order to improve performance further, we combine a classical deep neural network(DNN) component with the shallow model to be a deep model. The deep FiBiNET consistently outperforms the other state-of-the-art deep models such as DeepFM and extreme deep factorization machine(XdeepFM).