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

Parameter	Type	Distribution	Values	Selected
embedding_size	Integer	LogUniform	4 ≤ \(x\) ≤ 512	8
regularization	Float	LogUniform	0.0001 ≤ \(x\) ≤ 10	0.0435
learning_rate	Float	LogUniform	0.001 ≤ \(x\) ≤ 0.1	0.00786
reg_method	Categorical	Uniform	L2, AdamW	L2

Final Result

Searching selected the following configuration:

{
    'embedding_size': 8,
    'regularization': 0.04348568763678297,
    'learning_rate': 0.007860577021310463,
    'reg_method': 'L2',
    'epochs': 9
}

With these metrics:

{
    'RBP': 0.014196129440746583,
    'LogRBP': -0.4605459563536778,
    'NDCG': 0.17844322060908277,
    'RecipRank': 0.05514580220640624,
    'RMSE': 0.8223512184170895,
    'TrainTask': '3f3e3bae-5bf9-455d-af25-936807e5f8a4',
    'TrainTime': None,
    'TrainCPU': None,
    'max_epochs': 50,
    'done': True,
    'training_iteration': 9,
    'trial_id': 'ec487_00091',
    'date': '2025-05-05_06-15-09',
    'timestamp': 1746440109,
    'time_this_iter_s': 2.2098076343536377,
    'time_total_s': 22.274284601211548,
    'pid': 3582069,
    'hostname': 'CCI-ws21',
    'node_ip': '10.248.127.152',
    'config': {
        'embedding_size': 8,
        'regularization': 0.04348568763678297,
        'learning_rate': 0.007860577021310463,
        'reg_method': 'L2',
        'epochs': 9
    },
    'time_since_restore': 2.2098076343536377,
    'iterations_since_restore': 1
}

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?