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': 0.05470555539256399,
    'NDCG': 0.3550490042646473,
    'RecipRank': 0.10320164113505455,
    'RMSE': 0.7717265052197858,
    'TrainTask': '319c878a-1ee7-4590-80f5-48ad82502160',
    'TrainTime': None,
    'TrainCPU': None,
    'max_epochs': 30,
    'done': True,
    'training_iteration': 6,
    'trial_id': 'c2227_00042',
    'date': '2025-04-23_16-23-09',
    'timestamp': 1745439789,
    'time_this_iter_s': 173.11636066436768,
    'time_total_s': 1152.1899688243866,
    'pid': 291756,
    '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': 1152.1899688243866,
    '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?