  Learning effective feature crosses is the key behind building recommender
systems. However, the sparse and large feature space requires exhaustive search
to identify effective crosses. Deep & Cross Network (DCN) was proposed to
automatically and efficiently learn bounded-degree predictive feature
interactions. Unfortunately, in models that serve web-scale traffic with
billions of training examples, DCN showed limited expressiveness in its cross
network at learning more predictive feature interactions. Despite significant
research progress made, many deep learning models in production still rely on
traditional feed-forward neural networks to learn feature crosses
inefficiently.
  In light of the pros/cons of DCN and existing feature interaction learning
approaches, we propose an improved framework DCN-V2 to make DCN more practical
in large-scale industrial settings. In a comprehensive experimental study with
extensive hyper-parameter search and model tuning, we observed that DCN-V2
approaches outperform all the state-of-the-art algorithms on popular benchmark
datasets. The improved DCN-V2 is more expressive yet remains cost efficient at
feature interaction learning, especially when coupled with a mixture of
low-rank architecture. DCN-V2 is simple, can be easily adopted as building
blocks, and has delivered significant offline accuracy and online business
metrics gains across many web-scale learning to rank systems at Google.
