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.