SVD (Matrix Factorization)

Description

The SVD (Singular Value Decomposition) policy implements matrix factorization, often referring to iterative algorithms inspired by SVD used for collaborative filtering (like Funk SVD or SVD++). It decomposes the user-item interaction matrix into latent factors and potentially incorporates user/item biases (mean-centering tendencies) to improve prediction accuracy. It's typically trained using optimization techniques like Stochastic Gradient Descent (SGD).

Policy Type: svd Supports: embedding_policy, scoring_policy

Configuration Example

embedding_policy_svd.yaml

policy_configs:
  embedding_policy: # Can also be used under scoring
    policy_type: svd
    n_factors: 100        # Number of latent factors 
    n_epochs: 20          # Number of training iterations (epochs) for SGD
    biased: true          # Whether to include user/item bias terms 
    lr_all: 0.005         # Learning rate for SGD
    reg_all: 0.02         # Regularization strength for SG

References

• Koren, Y. (2008). Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. KDD. (Introduced SVD++ which includes biases and implicit influence).
• Funk, S. (2006). Netflix Update: Try This at Home. Blog post. (Popularized iterative matrix factorization with SGD for the Netflix Prize).