Parameter | Type | Distribution | Values | Selected |
---|---|---|---|---|
embedding_size | Integer | LogUniform | 4 ≤ \(x\) ≤ 512 | 77 |
regularization | Float | LogUniform | 0.0001 ≤ \(x\) ≤ 10 | 1.17 |
learning_rate | Float | LogUniform | 0.001 ≤ \(x\) ≤ 0.1 | 0.0357 |
reg_method | Categorical | Uniform | L2, AdamW | AdamW |
item_bias | Categorical | Uniform | True, False | True |
FlexMF WARP
This page analyzes the hyperparameter tuning results for the FlexMF scorer in implicit-feedback mode with WARP loss.
Parameter Search Space
Final Result
Searching selected the following configuration:
{ 'embedding_size': 77, 'regularization': 1.174755807374016, 'learning_rate': 0.035738365006726634, 'reg_method': 'AdamW', 'item_bias': True, 'epochs': 10 }
With these metrics:
{ 'RBP': 0.12193494701126657, 'NDCG': 0.38703757151254736, 'RecipRank': 0.3944036606436157, 'TrainTask': 'e3546880-077c-49de-8295-006c340471c7', 'TrainTime': None, 'TrainCPU': None, 'max_epochs': 50, 'done': False, 'training_iteration': 10, 'trial_id': '6cb7d_00039', 'date': '2025-04-21_17-59-27', 'timestamp': 1745272767, 'time_this_iter_s': 0.23075485229492188, 'time_total_s': 3.4539794921875, 'pid': 542019, 'hostname': 'CCI-ws21', 'node_ip': '10.248.127.152', 'config': { 'embedding_size': 77, 'regularization': 1.174755807374016, 'learning_rate': 0.035738365006726634, 'reg_method': 'AdamW', 'item_bias': True, 'epochs': 10 }, 'time_since_restore': 0.8159973621368408, 'iterations_since_restore': 3 }
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?