ALS Implicit

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

Parameter Search Space

Parameter	Type	Distribution	Values	Selected
embedding_size_exp	Integer	Uniform	3 ≤ \(x\) ≤ 10	5
regularization.user	Float	LogUniform	1e-05 ≤ \(x\) ≤ 1	0.538
regularization.item	Float	LogUniform	1e-05 ≤ \(x\) ≤ 1	0.302
weight	Float	Uniform	5 ≤ \(x\) ≤ 100	5

Final Result

Searching selected the following configuration:

{
    'embedding_size_exp': 5,
    'regularization': {'user': 0.5377113342545874, 'item': 0.3016285268860072},
    'weight': 5.003931155116192,
    'epochs': 7
}

With these metrics:

{
    'RBP': 0.10368340971386897,
    'DCG': 1.271514337921707,
    'NDCG': 0.3570058638329642,
    'RecipRank': 0.3349724574748492,
    'Hit10': 0.6605960264900662,
    'max_epochs': 30,
    'epoch_train_s': 0.043975158128887415,
    'epoch_measure_s': 1.042900734115392,
    'done': False,
    'training_iteration': 7,
    'trial_id': 'c8f6d195',
    'date': '2025-09-29_11-12-27',
    'timestamp': 1759158747,
    'time_this_iter_s': 1.091226577758789,
    'time_total_s': 7.585057258605957,
    'pid': 3284423,
    'hostname': 'CCI-ws21',
    'node_ip': '10.248.127.152',
    'config': {
        'embedding_size_exp': 5,
        'regularization': {'user': 0.5377113342545874, 'item': 0.3016285268860072},
        'weight': 5.003931155116192,
        'epochs': 7
    },
    'time_since_restore': 7.585057258605957,
    'iterations_since_restore': 7
}

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?