DCN-V2

2000년 도입 · 논문 3편에서 사용

DCN-V2 is an architecture for learning-to-rank that improves upon the original 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.

출처: DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

소개 논문: DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems