Parameter | Type | Distribution | Values | Selected |
---|---|---|---|---|
max_nbrs | Integer | Uniform | 2 ≤ \(x\) ≤ 50 | 48 |
min_nbrs | Integer | Uniform | 1 ≤ \(x\) ≤ 5 | 1 |
min_sim | Float | LogUniform | 1e-06 ≤ \(x\) ≤ 0.1 | 0.00138 |
UKNN Implicit
This page analyzes the hyperparameter tuning results for the ItemKNN scorer in implicit-feedback mode.
Parameter Search Space
Final Result
Searching selected the following configuration:
{'max_nbrs': 48, 'min_nbrs': 1, 'min_sim': 0.001379808386693685}
With these metrics:
{ 'RBP': 0.10932398176010141, 'NDCG': 0.36032996267360856, 'RecipRank': 0.3664214976179329, 'TrainTask': 'b6a1ab6f-a108-470b-a8d6-208aae2d3f10', 'TrainTime': 0.01956150800015166, 'TrainCPU': 0.025275999999999632, 'TestTask': '88af01f4-dca4-4e69-8ff7-802cdb4806b3', 'TestTime': 56.197305957000026, 'TestCPU': 57.993985, 'timestamp': 1742697946, 'checkpoint_dir_name': None, 'done': True, 'training_iteration': 1, 'trial_id': '81f6e_00038', 'date': '2025-03-22_22-45-46', 'time_this_iter_s': 56.56378650665283, 'time_total_s': 56.56378650665283, 'pid': 192935, 'hostname': 'CCI-ws21', 'node_ip': '10.248.127.152', 'config': {'max_nbrs': 48, 'min_nbrs': 1, 'min_sim': 0.001379808386693685}, 'time_since_restore': 56.56378650665283, 'iterations_since_restore': 1, 'experiment_tag': '38_max_nbrs=48,min_nbrs=1,min_sim=0.0014' }
Parameter Behavior
Neighborhood Size
The neighborhood size is key argument to consider, let’s look at RMSE with repspect to that:
Filtering
There are two filtering parameters we also test — minimum neighbors and mininmum simialrity.