Feature engineering has been the key to the success of many prediction models. However, the process is nontrivial and o en 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 e cient in learning all types of cross features. In this paper, we propose the Deep & Cross Network (DCN) which keeps the bene ts of a DNN model, and beyond that, it introduces a novel cross network that is more e cient 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 classi cation dataset, in terms of both model accuracy and memory usage.
DCN REF introduces a novel cross network which is more efficient in learning feature interactions explicitly by apply feature crossing at each layer.
Deep & Cross Network for Ad Click Predictions
ADKDD'17
Computer Science Mathematics