  Click-Through Rate (CTR) prediction is one of the most important and
challenging in calculating advertisements and recommendation systems. To build
a machine learning system with these data, it is important to properly model
the interaction among features. However, many current works calculate the
feature interactions in a simple way such as inner product and element-wise
product. This paper aims to fully utilize the information between features and
improve the performance of deep neural networks in the CTR prediction task. In
this paper, we propose a Feature Interaction based Neural Network (FINN) which
is able to model feature interaction via a 3-dimention relation tensor. FINN
provides representations for the feature interactions on the the bottom layer
and the non-linearity of neural network in modelling higher-order feature
interactions. We evaluate our models on CTR prediction tasks compared with
classical baselines and show that our deep FINN model outperforms other
state-of-the-art deep models such as PNN and DeepFM. Evaluation results
demonstrate that feature interaction contains significant information for
better CTR prediction. It also indicates that our models can effectively learn
the feature interactions, and achieve better performances in real-world
datasets.
