FlexMF WARP

This page analyzes the hyperparameter tuning results for the FlexMF scorer in implicit-feedback mode with WARP loss.

Parameter Search Space

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Parameter Type Distribution Values Selected
embedding_size_exp Integer Uniform 3 ≤ \(x\) ≤ 10 5
regularization Float LogUniform 0.0001 ≤ \(x\) ≤ 10 0.126
learning_rate Float LogUniform 0.001 ≤ \(x\) ≤ 0.1 0.00297
reg_method Categorical Uniform L2, AdamW AdamW
item_bias Categorical Uniform True, False True

Final Result

Searching selected the following configuration:

{
    'embedding_size_exp': 5,
    'regularization': 0.12635528554790926,
    'learning_rate': 0.002968879172151884,
    'reg_method': 'AdamW',
    'item_bias': True,
    'epochs': 16
}

With these metrics:

{
    'RBP': 0.24155438907443896,
    'DCG': 11.760526561110588,
    'NDCG': 0.47866059157942203,
    'RecipRank': 0.43078719832874235,
    'Hit10': 0.674901185770751,
    'max_epochs': 50,
    'epoch_train_s': 13.15165871893987,
    'epoch_measure_s': 7.955529009923339,
    'done': True,
    'training_iteration': 16,
    'trial_id': '4a1d559e',
    'date': '2025-09-29_15-21-54',
    'timestamp': 1759173714,
    'time_this_iter_s': 21.11275315284729,
    'time_total_s': 343.76303362846375,
    'pid': 3534603,
    'hostname': 'CCI-ws21',
    'node_ip': '10.248.127.152',
    'config': {
        'embedding_size_exp': 5,
        'regularization': 0.12635528554790926,
        'learning_rate': 0.002968879172151884,
        'reg_method': 'AdamW',
        'item_bias': True,
        'epochs': 16
    },
    'time_since_restore': 60.31975746154785,
    'iterations_since_restore': 3
}

Parameter Analysis

Embedding Size

The embedding size is the hyperparameter that most affects the model’s fundamental logic, so let’s look at performance as a fufnction of it:

Learning Parameters

Iteration Completion

How many iterations, on average, did we complete?

How did the metric progress in the best result?

How did the metric progress in the longest results?