Methods › General › Deep Tabular Learning › Wide&Deep
Wide&Deep
Introduced by Heng-Tze Cheng et al. in Wide & Deep Learning for Recommender Systems
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Wide&Deep jointly trains wide linear models and deep neural networks to combine the benefits of memorization and generalization for real-world recommender systems. In summary, the wide component is a generalized linear model. The deep component is a feed-forward neural network. The deep and wide components are combined using a weighted sum of their output log odds as the prediction. This is then fed to a logistic loss function for joint training, which is done by back-propagating the gradients from the output to both the wide and deep part of the model simultaneously using mini-batch stochastic optimization. The AdaGrad optimizer is used for the wider part. The combined model is illustrated in the figure (center).
Papers archive 2025-07-28
7 shown of 7, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Knowledge Graph Driven Recommendation System Algorithm 1 Dec 2023 · 0 repositories · arXiv:2401.10244
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TFNet: Multi-Semantic Feature Interaction for CTR Prediction 29 Jun 2020 · 0 repositories · arXiv:2006.15939
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Click-Through Rate Prediction with the User Memory Network 9 Jul 2019 · 1 repository · arXiv:1907.04667
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Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction 4 Dec 2018 · 2 repositories · arXiv:1812.01353
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Neural Factorization Machines for Sparse Predictive Analytics 16 Aug 2017 · 6 repositories · arXiv:1708.05027
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Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks 15 Aug 2017 · 8 repositories · arXiv:1708.04617
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Wide & Deep Learning for Recommender Systems 24 Jun 2016 · 39 repositories · arXiv:1606.07792Syntology ran 0 of 5 samples · 5 unverified · 5 pointer-only (licence)
Tasks archive 2025-07-28
10 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Click-Through Rate Prediction | 4 |
| Recommendation Systems | 3 |
| Prediction | 2 |
| Deep Learning | 1 |
| Feature Engineering | 1 |
| Graph Neural Network | 1 |
| Link Prediction | 1 |
| Memorization | 1 |
| News Recommendation | 1 |
| regression | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections