DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
By Ruoxi Wang, Rakesh Shivanna, Derek Zhiyuan Cheng, Sagar Jain, Dong Lin, Lichan Hong, Ed H. Chi. The Web Conference, 2020
865 citations recorded by the source corpus.
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. Our code and tutorial are open-sourced as part of TensorFlow Recommenders (TFRS)1.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/e7744bd79b314a59686344515fa471e5acdc5eb4
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In Artificial Intelligence
- Ivan Rubachev
- Yu. V. Gorishniy
- Valentin Khrulkov
- Artem Babenko
- Jongsoo Park
- Dheevatsa Mudigere
- Jiyan Yang
- Liangchen Luo
Citations in the corpus
- DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction (2022, background): “Deep Cross Net (DCN) is a widely used feature interaction Module in CTR prediction tasks [39].”
- Revisiting Deep Learning Models for Tabular Data (2021, methodology): “• DCN V2 (Wang et al., 2020a).”
- Don't Contrast the Impossible: Region-Constrained Batching for Contrastive User Modeling on a Local Community Platform (2026, unknown): “For home feed ranking and display ads ranking, we use DCN-based models [21] and add the learned user representation as an additional feature.”