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.
The Cross Network comprises all the cross terms of degree from 1 to l+1.
We show that, with only O(d^n) parameters, the cross network contains all the cross terms occurring in the polynomial of the same degree, with each term’s coefficient distinct from each other.
The cross network shares the spirit of parameter sharing as the FM model and further extends it to a deeper structure.
The weight of xixj is the multiplications of parameters from the sets
and
