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Ngram (Sequential)

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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 The APIs have changed and information may be outdated.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 Ngram policy implements a simple, frequency-based sequential model. It predicts the next item based on the conditional probability derived from counts of the immediately preceding n-1 items (the n-gram context). It's effective at capturing short-term co-occurrence patterns in user behavior sequences.

Policy Type: ngram Supports: scoring_policy

Configuration Example​

policy_configs:
  scoring_policy: 
    policy_type: ngram
    n: 3                  # Sequence length (e.g., 3 for trigrams - uses last 2 items)
    laplace_smoothing: 0.05 # Smoothing factor to handle unseen sequences (avoids zero probability)

References​

  • Concept based on N-gram language models. See: Jurafsky, D., & Martin, J. H. (2009). Speech and Language Processing. Prentice Hall.
  • Wikipedia: Word n-gram language model