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EASE (Autoencoder)

Description​

The EASE (Embarrassingly Shallow Autoencoder) policy implements a simple linear autoencoder for item-based collaborative filtering using a closed-form solution. It learns an item-item similarity matrix B by minimizing ||X - X * B||^2 subject to diag(B)=0 and L2 regularization on B. The solution avoids iterative optimization, making it computationally efficient to compute the similarity matrix B once. Predictions for a user are made by multiplying their interaction vector by B.

Policy Type: ease Supports: embedding_policy, scoring_policy

Configuration Example​

embedding_policy_ease.yaml
policy_configs:
  embedding_policy: # Or scoring_policy
    policy_type: ease
    # Training Hyperparameters
    batch_size: 512         # Samples per training batch
    n_epochs: 20            # Number of training epochs
    lr: 0.1                 # Learning rate
    # Model Hyperparameters
    factors: 10             # Rank (number of latent factors) for the low-rank part

Reference​