Description
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.
Papers Using This Method
UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style2024-02-16Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models2021-11-14DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems2020-08-19