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ALS (Matrix Factorization)

Description​

The ALS (Alternating Least Squares) policy implements matrix factorization for collaborative filtering, particularly well-suited for implicit feedback datasets. It decomposes the user-item interaction matrix into user and item latent factor matrices (P and Q) by iteratively solving regularized least squares problems, alternating between fixing P and solving for Q, then fixing Q and solving for P. It commonly incorporates confidence weighting (like BM25 or a linear weighting scheme) to handle implicit feedback where observed interactions have higher confidence than unobserved ones.

Policy Type: als Supports: embedding_policy, scoring_policy

Hyperparameter tuning​

  • factors: Number of latent factors in the matrix factorization.
  • regularization: Regularization parameter to prevent overfitting.
  • bm25: Whether to use BM25 weighting scheme.
  • bm25_k1: BM25 coefficient for item frequency factor.
  • bm25_b: BM25 coefficient for document length factor.
  • use_features: Whether to use entity features in the model.

V1 API

policy_configs:
  embedding_policy: # Can also be used under scoring_policy
    policy_type: als
    factors: 64           # Number of latent factors (embedding dimension)
    regularization: 0.1  # Regularization parameter (lambda)
    iterations: 15        # Number of ALS iterations
    # Implicit Feedback Handling
    bm25: true            # Whether to use BM25 weighting (alternative: simple alpha weighting)
    bm25_k1: 1.2          # BM25 k1 parameter (if bm25=true)
    bm25_b: 0.75          # BM25 b parameter (if bm25=true)

Usage​

Use this model when:​

  • You have implicit feedback data (clicks, views, purchases) rather than explicit ratings
  • You need a simple, interpretable collaborative filtering approach
  • You want to learn item-item similarity from user interaction patterns
  • You have a large dataset and need efficient training
  • You're building a baseline model before trying more complex approaches

Choose a different model when:​

  • You have rich item metadata or text features that you want to leverage (use Two-Tower or BeeFormer instead)
  • You need to model sequential patterns or temporal dependencies (use sequential models like SASRec)
  • You have very sparse data with limited interactions per user/item
  • You need the highest possible accuracy and can invest in more complex models

Use cases​

  • E-commerce product recommendations (clothing, electronics, books)
  • Content recommendations (articles, videos, music)
  • App recommendations
  • General collaborative filtering scenarios with implicit feedback

Reference​

  • Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative Filtering for Implicit Feedback Datasets. ICDM.
  • Takács, G., et al. (2011). Applications of the Stochastic Gradient Descent Method for Matrix Factorization in Recommendation Systems. RecSys. (Though ALS is distinct, SGD is another common MF solver).