  Click-Through Rate prediction is an important task in recommender systems,
which aims to estimate the probability of a user to click on a given item.
Recently, many deep models have been proposed to learn low-order and high-order
feature interactions from original features. However, since useful interactions
are always sparse, it is difficult for DNN to learn them effectively under a
large number of parameters. In real scenarios, artificial features are able to
improve the performance of deep models (such as Wide & Deep Learning), but
feature engineering is expensive and requires domain knowledge, making it
impractical in different scenarios. Therefore, it is necessary to augment
feature space automatically. In this paper, We propose a novel Feature
Generation by Convolutional Neural Network (FGCNN) model with two components:
Feature Generation and Deep Classifier. Feature Generation leverages the
strength of CNN to generate local patterns and recombine them to generate new
features. Deep Classifier adopts the structure of IPNN to learn interactions
from the augmented feature space. Experimental results on three large-scale
datasets show that FGCNN significantly outperforms nine state-of-the-art
models. Moreover, when applying some state-of-the-art models as Deep
Classifier, better performance is always achieved, showing the great
compatibility of our FGCNN model. This work explores a novel direction for CTR
predictions: it is quite useful to reduce the learning difficulties of DNN by
automatically identifying important features.
