mlsanityData to 14 Oct 2026

Deep & Cross Network for Ad Click Predictions

arXiv 1708.05123 ↗First posted 17 Aug 2017cs.LG stat.MLcited by 393

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Abstract (arXiv)

Feature engineering has been the key to the success of many prediction models. However, the process is non-trivial and often requires manual feature engineering or exhaustive searching. DNNs are able to automatically learn feature interactions; however, they generate all the interactions implicitly, and are not necessarily efficient in learning all types of cross features. In this paper, we propose the Deep & Cross Network (DCN) which keeps the benefits of a DNN model, and beyond that, it introduces a novel cross network that is more efficient in learning certain bounded-degree feature interactions. In particular, DCN explicitly applies feature crossing at each layer, requires no manual feature engineering, and adds negligible extra complexity to the DNN model. Our experimental results have demonstrated its superiority over the state-of-art algorithms on the CTR prediction dataset and dense classification dataset, in terms of both model accuracy and memory usage.

Key facts. Cited by 393 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Aug 2017.

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Citing papers per year: 2020: 1; 2021: 63; 2022: 60; 2023: 69; 2024: 63; 2025: 75; 2026: 62.

393 papers.

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  1. 2021Revisiting Deep Learning Models for Tabular Datacited by 740
  2. 2022On Embeddings for Numerical Features in Tabular Deep Learningcited by 186
  3. 2023RanPAC: Random Projections and Pre-trained Models for Continual Learningcited by 164
  4. 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
  5. 2022TransTab: Learning Transferable Tabular Transformers Across Tablescited by 132
  6. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  7. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 77
  8. 2022CTAB-GAN+: Enhancing Tabular Data Synthesiscited by 70
  9. 2024HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modelingcited by 63
  10. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  11. 2023ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Modelscited by 54
  12. 2022Transfer Learning with Deep Tabular Modelscited by 52
38 more
  1. 2024TALENT: A Tabular Analytics and Learning Toolboxcited by 52
  2. 2021End-to-End User Behavior Retrieval in Click-Through RatePrediction Modelcited by 45
  3. 2024CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendationcited by 44
  4. 2022Revisiting Pretraining Objectives for Tabular Deep Learningcited by 43
  5. 2022T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interactioncited by 41
  6. 2025TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problemscited by 36
  7. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  8. 2023TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterestcited by 35
  9. 2023ExcelFormer: A neural network surpassing GBDTs on tabular datacited by 34
  10. 2021Deep Learning for Click-Through Rate Estimationcited by 33
  11. 2021FM2: Field-matrixed Factorization Machines for Recommender Systemscited by 31
  12. 2022Click-Through Rate Prediction in Online Advertising: A Literature Reviewcited by 31
  13. 2022AutoField: Automating Feature Selection in Deep Recommender Systemscited by 30
  14. 2024TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishoucited by 30
  15. 2021HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Frameworkcited by 28
  16. 2023ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Predictioncited by 27
  17. 2021ARM-Net: Adaptive Relation Modeling Network for Structured Datacited by 24
  18. 2022Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Predictioncited by 24
  19. 2021SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarioscited by 23
  20. 2021Top-N Recommendation with Counterfactual User Preference Simulationcited by 21
  21. 2023Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Metacited by 21
  22. 2022Merlin HugeCTR: GPU-accelerated Recommender System Training and Inferencecited by 20
  23. 2023Embedding in Recommender Systems: A Surveycited by 20
  24. 2024A Survey on Diffusion Models for Recommender Systemscited by 20
  25. 2026Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agentscited by 19
  26. 2022A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Predictioncited by 18
  27. 2023Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendationscited by 18
  28. 2024Non-autoregressive Generative Models for Reranking Recommendationcited by 18
  29. 2025ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendationcited by 18
  30. 2022Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneitycited by 17
  31. 2022Billion-user Customer Lifetime Value Prediction: An Industrial-scale Solution from Kuaishoucited by 17
  32. 2024Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graphcited by 16
  33. 2021User Response Prediction in Online Advertisingcited by 15
  34. 2021Simple Modifications to Improve Tabular Neural Networkscited by 14
  35. 2022CL4CTR: A Contrastive Learning Framework for CTR Predictioncited by 14
  36. 2023Revisiting Neural Retrieval on Acceleratorscited by 14
  37. 2023MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systemscited by 14
  38. 2023Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systemscited by 14

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