Parameter | Type | Distribution | Values | Selected |
---|---|---|---|---|
embedding_size | Integer | LogUniform | 4 ≤ \(x\) ≤ 512 | 9 |
regularization | Float | LogUniform | 0.0001 ≤ \(x\) ≤ 10 | 0.0392 |
learning_rate | Float | LogUniform | 0.001 ≤ \(x\) ≤ 0.1 | 0.0029 |
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': 9, 'regularization': 0.039164122203056186, 'learning_rate': 0.002902394647945465, 'reg_method': 'AdamW', 'item_bias': True, 'epochs': 22 }
With these metrics:
{ 'RBP': 0.2198006871963069, 'NDCG': 0.4408849768264969, 'RecipRank': 0.3959630952900485, 'TrainTask': '1c7f3ca7-0666-4b47-a492-c924844ee197', 'TrainTime': None, 'TrainCPU': None, 'max_epochs': 50, 'done': True, 'training_iteration': 22, 'trial_id': 'cea44_00094', 'date': '2025-04-23_00-11-43', 'timestamp': 1745381503, 'time_this_iter_s': 22.314486265182495, 'time_total_s': 863.8184952735901, 'pid': 1207768, 'hostname': 'CCI-ws21', 'node_ip': '10.248.127.152', 'config': { 'embedding_size': 9, 'regularization': 0.039164122203056186, 'learning_rate': 0.002902394647945465, 'reg_method': 'AdamW', 'item_bias': True, 'epochs': 22 }, 'time_since_restore': 432.60910964012146, 'iterations_since_restore': 14 }
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?