|
--- |
|
library_name: keras |
|
tags: |
|
- collaborative-filtering |
|
- recommender |
|
- tabular-classification |
|
license: |
|
- cc0-1.0 |
|
--- |
|
|
|
## 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> |
