Wide and Deep learning for Recommender Systems

Rohan Anil

Abstract: Generalized linear models with nonlinear feature transfor- mations are widely used for large-scale regression and clas- sification problems with sparse inputs. Memorization of fea- ture interactions through a wide set of cross-product feature transformations are effective and interpretable, while gener- alization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize bet- ter 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 inter- actions 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 mem- orization 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 acquisi- tions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow.