  With the rapid development of online advertising and recommendation systems,
click-through rate prediction is expected to play an increasingly important
role.Recently many DNN-based models which follow a similar Embedding&MLP
paradigm have been proposed, and have achieved good result in image/voice and
nlp fields. In these methods the Wide&Deep model announced by Google plays a
key role.Most models first map large scale sparse input features into
low-dimensional vectors which are transformed to fixed-length vectors, then
concatenated together before being fed into a multilayer perceptron (MLP) to
learn non-linear relations among input features. The number of trainable
variables normally grow dramatically the number of feature fields and the
embedding dimension grow. It is a big challenge to get state-of-the-art result
through training deep neural network and embedding together, which falls into
local optimal or overfitting easily. In this paper, we propose an Structured
Semantic Model (SSM) to tackles this challenge by designing a orthogonal base
convolution and pooling model which adaptively learn the multi-scale base
semantic representation between features supervised by the click label.The
output of SSM are then used in the Wide&Deep for CTR prediction.Experiments on
two public datasets as well as real Weibo production dataset with over 1
billion samples have demonstrated the effectiveness of our proposed approach
with superior performance comparing to state-of-the-art methods.
