Name: Mohamed Abdelhamid

Email: [email protected]

Group: BS20-AAI

This repository hosts the implementation of a movie recommender system developed as part of a Practical Machine Learning and Deep Learning course project. The system utilizes the MovieLens 100K dataset to suggest movies.

The MovieLens 100K dataset includes 100,000 movie ratings from 943 users across 1682 movies. It features user demographics (age, gender, occupation, zip code) and movie details (genres, titles).

The project employs a Matrix Factorization approach to build the recommender system. This collaborative filtering method decomposes the user-item interaction matrix into user and movie embeddings to predict user preferences.

• Root Mean Square Error (RMSE): Measures the average magnitude of prediction errors.
• Precision at Top-5: Evaluates the accuracy of the top five recommendations made to the users.

movie-recommender-system
├── README.md               # The top-level README
│
├── data
│   ├── external            # Data from third party sources
│   ├── interim             # Intermediate data that has been transformed.
│   └── raw                 # The original, immutable data
│
├── models                  # Trained and serialized models, final checkpoints
│
├── notebooks               #  Jupyter notebooks.
│                               
│                                        
│ 
├── references              # Data dictionaries, manuals, and all other explanatory materials.
│
├── reports
│   ├── figures             # Generated graphics and figures to be used in reporting
│   └── final_report.pdf    # Report containing data exploration, solution exploration, training process, and evaluation
│
└── benchmark
    ├── data                # dataset used for evaluation 
    └── evaluate.py         # script that performs evaluation of the given model

• RMSE Score: 1.2
• Precision at Top-5: 0.013

To run the project:

1. Clone the repository.
2. Install required dependencies.
3. Run the Jupyter notebooks in the notebooks directory for data exploration and model training.