DCN-V2
DCN-V2
**DCN-V2** is an architecture for learning-to-rank that improves upon the original [DCN](http://paperswithcode.com/method/dcn) model. It first learns explicit feature interactions of the inputs (typically the embedding layer) through cross layers, and then combines with a deep network to learn complementary implicit interactions. The core of DCN-V2 is the cross layers, which inherit the simple structure of the cross network from DCN, however it is significantly more expressive at learning explicit and bounded-degree cross features.
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
https://paperswithcode.com/paper/dcn-m-improved-deep-cross-network-for-feature
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems