| library_name: tf-keras | |
| license: | |
| - cc0-1.0 | |
| tags: | |
| - collaborative-filtering | |
| - recommender | |
| - tabular-classification | |
| ## Model description | |
| This repo contains the model and the notebook on [how to build and train a Keras model for Collaborative Filtering for Movie Recommendations](https://keras.io/examples/structured_data/collaborative_filtering_movielens/). | |
| Full credits to [Siddhartha Banerjee](https://twitter.com/sidd2006). | |
| ## Intended uses & limitations | |
| 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 | |
| <details> | |
| <summary>View Model Plot</summary> | |
|  | |
| </details> |