Skip to main content

ELSA (Autoencoder)

warning

This is an article from the Shaped 1.0 documentation. The APIs have changed and information may be outdated. Go to Shaped 2.0 docs

Description​

The ELSA (Efficient Latent Sparse Autoencoder) policy implements a scalable shallow linear autoencoder designed for implicit feedback collaborative filtering. It learns item-item relationships by reconstructing user interaction vectors. To improve scalability over models like EASE, it uses a factorized hidden layer structure (low-rank plus sparse).

Policy Type: elsa Supports: embedding_policy, scoring_policy

Configuration Example​

policy_configs:
  embedding_policy: # Or scoring_policy
    policy_type: elsa
    # 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​