Based on a user and movies they have rated highly in the past, this model outputs the predicted rating a user would give to a movie they haven't seen yet (between 0-1). This information can be used to find out the top recommended movies for this user.

Training and evaluation data

The dataset consists of user's ratings on specific movies. It also consists of the movie's specific genres.

Training procedure

The model was trained for 5 epochs with a batch size of 64.

Training hyperparameters

The following hyperparameters were used during training:

optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32

Training Metrics

Epochs	Train Loss	Validation Loss
1	0.637	0.619
2	0.614	0.616
3	0.609	0.611
4	0.608	0.61
5	0.608	0.609

Model Plot

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oloberin/olober

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training Metrics

Model Plot

oloberin/
olober