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Wide & Deep (Neural Scoring)

Description​

The Wide & Deep policy implements the Wide & Deep Learning model, which jointly trains a wide linear model and a deep neural network (DNN) to combine the benefits of memorization and generalization.

  • Wide Part: A generalized linear model often fed with raw sparse features and manually engineered cross-product features to effectively memorize specific feature combinations.
  • Deep Part: A standard MLP fed with dense embeddings (learned from categorical features) and potentially normalized dense features to generalize by learning complex, non-linear patterns.

The outputs are typically summed before the final prediction layer.

Policy Type: wide-deep Supports: scoring_policy

Premium Model

This model requires the Standard Plan or higher.

Hyperparameter tuning​

  • val_split: Fraction of training data to use for validation.
  • n_epochs: Number of complete passes through the training data.
  • num_workers

V1 API

policy_configs:
  scoring_policy:
    policy_type: wide-deep
    # Architecture
    deep_hidden_units: [256, 128, 64] # Layer sizes for the deep MLP component
    activation_fn: "relu"             # Activation for deep layers (e.g., "relu", "sigmoid")
    # Training Control
    val_split: 0.1                    # Proportion of data for validation during training
    n_epochs: 10                      # Number of training epochs

Usage​

Use this model when:​

  • You want to combine memorization of specific feature combinations with generalization to unseen patterns
  • You have the ability to engineer meaningful cross-product features for the wide part
  • You have rich categorical features that can be embedded for the deep part
  • You are building recommendation or search ranking systems similar to app store or content feeds

Choose a different model when:​

  • You do not want to maintain manual feature engineering for the wide component (use DeepFM instead)
  • You prefer tree-based models with simpler feature engineering workflows (use LightGBM or XGBoost)
  • You primarily need retrieval-stage embeddings rather than point-wise scoring (use Two-Tower, ALS, or BeeFormer)
  • You need to model detailed sequential user behavior (use SASRec, BERT4Rec, or other sequential models)

Use cases​

  • App recommendation and store ranking (apps, games, extensions)
  • Personalized content ranking for feeds, homepages, and carousels
  • Search ranking where memorized query–item patterns and generalization both matter
  • CTR prediction and conversion modeling in environments with rich, hand-crafted cross-features

Reference​