https://github.com/samdc73/flickpick

Movie Recommendation System using PyTorch and Collaborative Filtering and trained on the MovieLens 25M dataset
https://github.com/samdc73/flickpick

jupyter-notebook movielens python pytroch recommender-system

Last synced: about 8 hours ago
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Movie Recommendation System using PyTorch and Collaborative Filtering and trained on the MovieLens 25M dataset

• Host: GitHub
• URL: https://github.com/samdc73/flickpick
• Owner: SamDc73
• Created: 2024-06-17T05:15:46.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-19T21:26:00.000Z (5 months ago)
• Last Synced: 2024-06-20T10:47:11.634Z (5 months ago)
• Topics: jupyter-notebook, movielens, python, pytroch, recommender-system
• Language: Jupyter Notebook
• Homepage:
• Size: 21.2 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Movie Recommendation System with PyTorch

This repository contains Jupyter notebooks for training and tuning a movie recommendation system using PyTorch and the MovieLens dataset.

## Notebooks

### 1. main.ipynb

This notebook contains the main code for training the movie recommendation model. It includes:

- Implementing a collaborative filtering approach using matrix factorization, specifically the Neural Collaborative Filtering (NCF) algorithm.

- Employing techniques such as embedding layers, fully connected layers with non-linear activations, dropout regularization, and optimization algorithms like Adam to capture complex user-movie interactions and prevent overfitting.

- Evaluating the trained model's performance useing Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and generating personalized movie recommendations based on the learned user and movie embeddings.

### 2. tuning.ipynb

This notebook focuses on hyperparameter tuning for the movie recommendation model, but due to computational resource limitations, the code in this notebook was written but not executed. It includes:

- Implementing a grid search approach to explore different combinations of hyperparameters.

- Utilizing k-fold cross-validation technique to evaluate the model's performance across different hyperparameter settings.

- Identifying the best-performing hyperparameter configuration based on the cross-validation results.

## Models

- The trained movie recommendation model has been serialized and saved in two different formats: PyTorch model checkpoint (.pth) and ONNX (Open Neural Network Exchange) format (.onnx).

- PyTorch model checkpoint (final_model_state_dict.pth): The model's parameters have been converted to half-precision (FP16).

- ONNX format (model.onnx) has been exported using torch.onnx.export().

## Dataset

- used the MovieLens 25M Dataset which can be found [here](https://grouplens.org/datasets/movielens/)

## Prerequisites

- Python 3.6+

- Jupyter Notebook

- PyTorch

- Additional Python libraries: pandas, numpy, matplotlib, tqdm, scikit-learn.