[1606.07792] Wide & Deep Learning for Recommender Systems

Combining wide linear features with deep neural nets - Paper:  Code:

  • Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
  • Deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features.
  • In the 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.
  • Deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank.
  • Abstract: Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs.

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@graphific: “Combining wide linear features with deep neural nets – Paper: Code:”

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[1606.07792] Wide & Deep Learning for Recommender Systems