mlsanityData to 7 Oct 2026

Wide & Deep Learning for Recommender Systems

arXiv 1606.07792 ↗First posted 24 Jun 2016cs.LG cs.IR stat.MLcited by 745

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Abstract (arXiv)

Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow.

Key facts. Cited by 745 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2016.

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Citing papers per year: 2020: 1; 2021: 138; 2022: 135; 2023: 158; 2024: 121; 2025: 100; 2026: 92.

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  1. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  2. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  3. 2023Recommender Systems with Generative Retrievalcited by 191
  4. 2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendationscited by 184
  5. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  6. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
  7. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  8. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 116
  9. 2023How to Index Item IDs for Recommendation Foundation Modelscited by 102
  10. 2024LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuningcited by 88
  11. 2021Label Leakage and Protection in Two-party Split Learningcited by 87
  12. 2021One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Predictioncited by 78
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  1. 2022Recommendation Unlearningcited by 71
  2. 2023DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Modelcited by 64
  3. 2021YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimizationcited by 61
  4. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  5. 2023Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendationscited by 50
  6. 2024ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Modelcited by 48
  7. 2024TALENT: A Tabular Analytics and Learning Toolboxcited by 46
  8. 2021End-to-End User Behavior Retrieval in Click-Through RatePrediction Modelcited by 45
  9. 2024CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendationcited by 44
  10. 2025Agentic Web: Weaving the Next Web with AI Agentscited by 43
  11. 2021Learnable Embedding Sizes for Recommender Systemscited by 42
  12. 2021Data Poisoning Attacks to Deep Learning Based Recommender Systemscited by 41
  13. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  14. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  15. 2023UP5: Unbiased Foundation Model for Fairness-aware Recommendationcited by 38
  16. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  17. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  18. 2023TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterestcited by 35
  19. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  20. 2021Device-Cloud Collaborative Learning for Recommendationcited by 34
  21. 2022MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Taskscited by 34
  22. 2021Deep Learning for Click-Through Rate Estimationcited by 33
  23. 2022Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Modelscited by 33
  24. 2023ExcelFormer: A neural network surpassing GBDTs on tabular datacited by 33
  25. 2021FM2: Field-matrixed Factorization Machines for Recommender Systemscited by 31
  26. 2021Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertisingcited by 31
  27. 2022Click-Through Rate Prediction in Online Advertising: A Literature Reviewcited by 31
  28. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 31
  29. 2022AutoField: Automating Feature Selection in Deep Recommender Systemscited by 30
  30. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  31. 2023Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossingcited by 30
  32. 2024TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishoucited by 30
  33. 2022User-Centric Conversational Recommendation with Multi-Aspect User Modelingcited by 29
  34. 2023LLMRec: Benchmarking Large Language Models on Recommendation Taskcited by 29
  35. 2023On the Embedding Collapse when Scaling up Recommendation Modelscited by 29
  36. 2021DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendationcited by 28
  37. 2021HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Frameworkcited by 28
  38. 2022RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendationcited by 28

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