Bias Predictor

This page analyzes the behavior and hyperparameter search for the Bias scoring model on MLLT.

Parameter Search Space

Parameter	Type	Distribution	Values	Selected
damping.user	Float	LogUniform	0.1 ≤ \(x\) ≤ 100	5.82
damping.item	Float	LogUniform	0.1 ≤ \(x\) ≤ 100	8.59

Final Result

Searching selected the following configuration:

{'damping': {'user': 5.818752728872168, 'item': 8.590236126993592}}

With these metrics:

{
    'RBP': 0.07398996273535478,
    'DCG': 8.756780917149218,
    'NDCG': 0.31943863068044864,
    'RecipRank': 0.211627323201809,
    'Hit10': 0.36716417910447763,
    'RMSE': 0.8299161791801453,
    'timestamp': 1753783293,
    'checkpoint_dir_name': None,
    'done': True,
    'training_iteration': 1,
    'trial_id': '66be3540',
    'date': '2025-07-29_06-01-33',
    'time_this_iter_s': 28.87991189956665,
    'time_total_s': 28.87991189956665,
    'pid': 334742,
    'hostname': 'CCI-ws21',
    'node_ip': '10.248.127.152',
    'config': {'damping': {'user': 5.818752728872168, 'item': 8.590236126993592}},
    'time_since_restore': 28.87991189956665,
    'iterations_since_restore': 1,
    'experiment_tag': '14_item=8.5902,user=5.8188'
}

Metric Response

How does RMSE change with each setting independently?

User Damping

Item Damping

Best Configurations

Since this is an explicit-feedback rating prediction model, our primary search criteria is RMSE. The configuration with the best RMSE is:

	config.damping.user	config.damping.item	RBP	RMSE
Method
Random	5.818753	8.590236	0.07399	0.829916

If we instead searched for RBP, we would select:

	config.damping.user	config.damping.item	RBP	RMSE
Method
Random	0.829947	40.229626	0.081263	0.832278

Search Geometry

What is the geometry of the search space?