FlexMF Explicit

This page analyzes the hyperparameter tuning results for the FlexMF scorer in explicit-feedback mode (a biased matrix factorization model trained with PyTorch).

Parameter Search Space

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Parameter	Type	Distribution	Values	Selected
embedding_size_exp	Integer	Uniform	3 ≤ \(x\) ≤ 10	4
regularization	Float	LogUniform	0.0001 ≤ \(x\) ≤ 10	0.119
learning_rate	Float	LogUniform	0.001 ≤ \(x\) ≤ 0.1	0.00423
reg_method	Categorical	Uniform	L2, AdamW	AdamW

Final Result

Searching selected the following configuration:

{
    'embedding_size_exp': 4,
    'regularization': 0.11929652807453098,
    'learning_rate': 0.00422821861051453,
    'reg_method': 'AdamW',
    'epochs': 5
}

With these metrics:

{
    'RBP': 0.16137180240142998,
    'DCG': 10.092204826620334,
    'NDCG': 0.41872709490730176,
    'RecipRank': 0.33805246283700296,
    'Hit10': 0.5869565217391305,
    'RMSE': 0.7614558935165405,
    'max_epochs': 50,
    'epoch_train_s': 0.7170857610180974,
    'epoch_measure_s': 6.831948532955721,
    'done': True,
    'training_iteration': 5,
    'trial_id': '839f8c8c',
    'date': '2025-09-30_00-05-01',
    'timestamp': 1759205101,
    'time_this_iter_s': 7.5527966022491455,
    'time_total_s': 37.48297643661499,
    'pid': 3954354,
    'hostname': 'CCI-ws21',
    'node_ip': '10.248.127.152',
    'config': {
        'embedding_size_exp': 4,
        'regularization': 0.11929652807453098,
        'learning_rate': 0.00422821861051453,
        'reg_method': 'AdamW',
        'epochs': 5
    },
    'time_since_restore': 37.48297643661499,
    'iterations_since_restore': 5
}

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:

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?