# Wide Deep Recommender - [Wide & Deep Learning for Recommender Systems](https://arxiv.org/abs/1606.07792) - 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 - However, memorization and generalization are both important for recommender systems. - With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse [Features](Features.md) - However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. - jointly trained wide linear models and deep neural networks – to combine the benefits of memorization and generalization for recommender systems - Wide linear models can effectively memorize sparse feature interactions using cross-product feature transformations, while deep neural networks can generalize to previously unseen feature interactions through low dimensional embeddings - In other words, the fusion of wide and deep models combines the strengths of memorization and generalization, and provides us with better recommendation systems - The two models are trained jointly with the same [loss](../Tag%20Pages/loss.md) function. - Google Play Store