Parameter | Type | Distribution | Values | Selected |
---|---|---|---|---|
embedding_size | Integer | LogUniform | 4 ≤ \(x\) ≤ 512 | 330 |
regularization.user | Float | LogUniform | 1e-05 ≤ \(x\) ≤ 1 | 0.0903 |
regularization.item | Float | LogUniform | 1e-05 ≤ \(x\) ≤ 1 | 0.0392 |
damping.user | Float | LogUniform | 1e-12 ≤ \(x\) ≤ 100 | 1.88e-05 |
damping.item | Float | LogUniform | 1e-12 ≤ \(x\) ≤ 100 | 0.000165 |
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': 330, 'regularization': {'user': 0.09026204464706182, 'item': 0.039227647579904934}, 'damping': {'user': 1.8785553295225284e-05, 'item': 0.00016458494355190166}, 'epochs': 6 }
With these metrics:
{ 'RBP': 0.0016840378949438514, 'LogRBP': -2.592320890707648, 'NDCG': 0.1615156871747588, 'RecipRank': 0.01622092087443804, 'RMSE': 0.8121416182411427, 'TrainTask': '3f3b4b9b-fbb3-4590-8c30-ba30a5553e0d', 'TrainTime': None, 'TrainCPU': None, 'max_epochs': 30, 'done': True, 'training_iteration': 6, 'trial_id': '4c049cef', 'date': '2025-05-05_00-32-43', 'timestamp': 1746419563, 'time_this_iter_s': 4.655622959136963, 'time_total_s': 26.12876296043396, 'pid': 2797815, 'hostname': 'CCI-ws21', 'node_ip': '10.248.127.152', 'config': { 'embedding_size': 330, 'regularization': {'user': 0.09026204464706182, 'item': 0.039227647579904934}, 'damping': {'user': 1.8785553295225284e-05, 'item': 0.00016458494355190166}, 'epochs': 6 }, 'time_since_restore': 26.12876296043396, '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?