Parameter | Type | Distribution | Values | Selected |
---|---|---|---|---|
embedding_size | Integer | LogUniform | 4 ≤ \(x\) ≤ 512 | 182 |
regularization.user | Float | LogUniform | 1e-05 ≤ \(x\) ≤ 1 | 0.135 |
regularization.item | Float | LogUniform | 1e-05 ≤ \(x\) ≤ 1 | 0.0372 |
damping.user | Float | LogUniform | 1e-12 ≤ \(x\) ≤ 100 | 1.38e-06 |
damping.item | Float | LogUniform | 1e-12 ≤ \(x\) ≤ 100 | 4.15 |
ALS BiasedMF
This page analyzes the hyperparameter tuning results for biased matrix factorization with ALS.
Parameter Search Space
Final Result
Searching selected the following configuration:
{ 'embedding_size': 182, 'regularization': {'user': 0.13457932006936438, 'item': 0.03716261973856335}, 'damping': {'user': 1.3842050969711814e-06, 'item': 4.147629054926619}, 'epochs': 6 }
With these metrics:
{ 'RBP': 0.03617969967228035, 'LogRBP': 0.4749828697903258, 'NDCG': 0.30264294670384995, 'RecipRank': 0.11096809011223152, 'RMSE': 0.7983685081718532, 'TrainTask': 'fba78a57-9449-4303-af1e-a9d8a497eec1', 'TrainTime': None, 'TrainCPU': None, 'max_epochs': 30, 'done': True, 'training_iteration': 6, 'trial_id': '2d29da02', 'date': '2025-05-07_04-38-38', 'timestamp': 1746607118, 'time_this_iter_s': 272.09115052223206, 'time_total_s': 1640.7825350761414, 'pid': 157071, 'hostname': 'CCI-ws21', 'node_ip': '10.248.127.152', 'config': { 'embedding_size': 182, 'regularization': {'user': 0.13457932006936438, 'item': 0.03716261973856335}, 'damping': {'user': 1.3842050969711814e-06, 'item': 4.147629054926619}, 'epochs': 6 }, 'time_since_restore': 1640.7825350761414, '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?