mlsanityData to 7 Oct 2026

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

arXiv 2008.13535 ↗First posted 19 Aug 2020cs.IR cs.LG stat.MLcited by 326

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Abstract (arXiv)

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.

Key facts. Cited by 326 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Aug 2020.

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Citing papers per year: 2021: 8; 2022: 26; 2023: 55; 2024: 62; 2025: 94; 2026: 81.

326 papers.

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38 more
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  26. 2021Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performancecited by 18
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  28. 2022Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneitycited by 17
  29. 2021The King is Naked: on the Notion of Robustness for Natural Language Processingcited by 16
  30. 2023GFS: Graph-based Feature Synthesis for Prediction over Relational Databasescited by 16
  31. 2024Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendationcited by 16
  32. 2025MiniOneRec: An Open-Source Framework for Scaling Generative Recommendationcited by 16
  33. 2021User Response Prediction in Online Advertisingcited by 15
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  38. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14

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