ALS Implicit

This page analyzes the hyperparameter tuning results for the implicit-feedback ALS matrix factorization model.

Parameter Search Space

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Parameter	Type	Distribution	Values	Selected
embedding_size_exp	Integer	Uniform	3 ≤ \(x\) ≤ 10	4
regularization.user	Float	LogUniform	1e-05 ≤ \(x\) ≤ 1	0.00866
regularization.item	Float	LogUniform	1e-05 ≤ \(x\) ≤ 1	8.46e-05
weight	Float	Uniform	5 ≤ \(x\) ≤ 100	6.06

Final Result

Searching selected the following configuration:

{
    'embedding_size_exp': 4,
    'regularization': {'user': 0.008655513740073196, 'item': 8.45776550979749e-05},
    'weight': 6.059395797720178,
    'epochs': 5
}

With these metrics:

{
    'RBP': 0.21296189932217136,
    'DCG': 11.578181244976797,
    'NDCG': 0.4704901381918856,
    'RecipRank': 0.380232100012578,
    'Hit10': 0.6314229249011858,
    'max_epochs': 30,
    'epoch_train_s': 0.0938908769749105,
    'epoch_measure_s': 3.592203930951655,
    'done': False,
    'training_iteration': 5,
    'trial_id': 'b492fffe',
    'date': '2025-09-29_13-18-13',
    'timestamp': 1759166293,
    'time_this_iter_s': 3.6897239685058594,
    'time_total_s': 18.73239779472351,
    'pid': 3385876,
    'hostname': 'CCI-ws21',
    'node_ip': '10.248.127.152',
    'config': {
        'embedding_size_exp': 4,
        'regularization': {'user': 0.008655513740073196, 'item': 8.45776550979749e-05},
        'weight': 6.059395797720178,
        'epochs': 5
    },
    'time_since_restore': 18.73239779472351,
    '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?