IKNN Explicit

This page analyzes the hyperparameter tuning results for the ItemKNN scorer in explicit-feedback mode.

Parameter Search Space

Parameter Type Distribution Values Selected
max_nbrs Integer Uniform 2 ≤ \(x\) ≤ 50 15
min_nbrs Integer Uniform 1 ≤ \(x\) ≤ 5 2
min_sim Float LogUniform 1e-06 ≤ \(x\) ≤ 0.1 0.0201

Final Result

Searching selected the following configuration:

{'max_nbrs': 15, 'min_nbrs': 2, 'min_sim': 0.0200884187051134}

With these metrics:

{
    'RBP': 0.009405565921280324,
    'LogRBP': -0.8722136758827306,
    'NDCG': 0.1877911905584548,
    'RecipRank': 0.04345087776299538,
    'RMSE': 0.9164248133699099,
    'TrainTask': 'af3c4d78-d9c8-4def-9aa2-f7442cc7acce',
    'TrainTime': 0.058767487993463874,
    'TrainCPU': 0.06283199999999967,
    'TestTask': '59e29d14-044f-4bac-afa8-b8cfb9159099',
    'TestTime': 2.195783903996926,
    'TestCPU': 7.344220000000001,
    'timestamp': 1746651818,
    'checkpoint_dir_name': None,
    'done': True,
    'training_iteration': 1,
    'trial_id': '075e598d',
    'date': '2025-05-07_17-03-38',
    'time_this_iter_s': 2.428950071334839,
    'time_total_s': 2.428950071334839,
    'pid': 1224021,
    'hostname': 'gracehopper1',
    'node_ip': '192.168.225.60',
    'config': {'max_nbrs': 15, 'min_nbrs': 2, 'min_sim': 0.0200884187051134},
    'time_since_restore': 2.428950071334839,
    'iterations_since_restore': 1,
    'experiment_tag': '75_max_nbrs=15,min_nbrs=2,min_sim=0.0201'
}

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.