ALS BiasedMF

This page analyzes the hyperparameter tuning results for biased matrix factorization with ALS.

Parameter Search Space

Parameter	Type	Distribution	Values	Selected
embedding_size	Integer	LogUniform	4 ≤ \(x\) ≤ 512	463
regularization.user	Float	LogUniform	1e-05 ≤ \(x\) ≤ 1	0.795
regularization.item	Float	LogUniform	1e-05 ≤ \(x\) ≤ 1	0.00163
damping.user	Float	LogUniform	1e-12 ≤ \(x\) ≤ 100	9.99e-11
damping.item	Float	LogUniform	1e-12 ≤ \(x\) ≤ 100	3.17e-09

Final Result

Searching selected the following configuration:

{
    'embedding_size': 463,
    'regularization': {'user': 0.794561339814651, 'item': 0.0016328354077841572},
    'damping': {'user': 9.986183348529642e-11, 'item': 3.1733506385207053e-09},
    'epochs': 6
}

With these metrics:

{
    'RBP': 0.025367093580509502,
    'NDCG': 0.3719189012210346,
    'RecipRank': 0.06811542069912295,
    'RMSE': 0.7547133446024419,
    'TrainTask': 'c1bb8621-be1f-4624-a2c4-97f0d0848d17',
    'TrainTime': None,
    'TrainCPU': None,
    'max_epochs': 30,
    'done': True,
    'training_iteration': 6,
    'trial_id': '6093a_00042',
    'date': '2025-04-04_14-25-50',
    'timestamp': 1743791150,
    'time_this_iter_s': 66.01537322998047,
    'time_total_s': 370.63378834724426,
    'pid': 219666,
    'hostname': 'CCI-ws21',
    'node_ip': '10.248.127.152',
    'config': {
        'embedding_size': 463,
        'regularization': {'user': 0.794561339814651, 'item': 0.0016328354077841572},
        'damping': {'user': 9.986183348529642e-11, 'item': 3.1733506385207053e-09},
        'epochs': 6
    },
    'time_since_restore': 370.63378834724426,
    'iterations_since_restore': 6
}

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?