Wide Deep Recommender

• Wide & Deep Learning for Recommender Systems
• 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
• 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 function.
• Google Play Store