Skip to main content

SASRec (Sequential)

Description​

The SASRec (Self-Attentive Sequential Recommendation) policy utilizes the Transformer architecture's self-attention mechanism to model user interaction sequences. By weighing the importance of all previous items, it captures both short-term and long-range dependencies to predict the next item the user is likely to interact with.

Policy Type: sasrec Supports: embedding_policy, scoring_policy

Configuration Example​

scoring_policy_sasrec.yaml
policy_configs:
  scoring_policy: # Can also be used under embedding_policy
    policy_type: sasrec
    # Training Hyperparameters
    batch_size: 1000           # Samples per training batch
    n_epochs: 1                # Number of training epochs
    negative_samples_count: 2  # Negative samples per positive for contrastive loss
    learning_rate: 0.001       # Optimizer learning rate
    # Architecture Hyperparameters
    hidden_size: 64            # Dimensionality of hidden layers/embeddings
    n_heads: 2                 # Number of self-attention heads
    n_layers: 2                # Number of Transformer layers
    attn_dropout_prob: 0.2     # Dropout rate in attention mechanism
    hidden_act: "gelu"         # Activation function (e.g., "gelu", "relu")
    max_seq_length: 50         # Maximum input sequence length

Reference​