Title: Deep & Cross Network for Ad Click Predictions,
Abstract: Feature engineering has been the key to the success of many prediction
models. However, the process is non-trivial and often requires manual feature
engineering or exhaustive searching. DNNs are able to automatically learn
feature interactions; however, they generate all the interactions implicitly,
and are not necessarily efficient in learning all types of cross features. In
this paper, we propose the Deep & Cross Network (DCN) which keeps the benefits
of a DNN model, and beyond that, it introduces a novel cross network that is
more efficient in learning certain bounded-degree feature interactions. In
particular, DCN explicitly applies feature crossing at each layer, requires no
manual feature engineering, and adds negligible extra complexity to the DNN
model. Our experimental results have demonstrated its superiority over the
state-of-art algorithms on the CTR prediction dataset and dense classification
dataset, in terms of both model accuracy and memory usage.