- Heng-Tze Cheng
- Levent Koc
- Jeremiah Harmsen
- Tal Shaked
- Tushar Chandra
- Hrishi Aradhye
- Glen Anderson
- Greg Corrado
- Wei Chai
- Mustafa Ispir
- Rohan Anil
- Zakaria Haque
- Lichan Hong
- Vihan Jain
- Xiaobing Liu
- Hemal Shah
Abstract
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 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.
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