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---
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> |