  Advertising and feed ranking are essential to many Internet companies such as
Facebook and Sina Weibo. Among many real-world advertising and feed ranking
systems, click through rate (CTR) prediction plays a central role. There are
many proposed models in this field such as logistic regression, tree based
models, factorization machine based models and 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. On the one hand, the FiBiNET can dynamically learn the importance
of features via the Squeeze-Excitation network (SENET) mechanism; on the other
hand, it is able to effectively learn the feature interactions via bilinear
function. We conduct extensive experiments on two real-world 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).
