Movie-Recommender

File Structure

Collaborative Filtering

• Cosine Similarity Based
  • User-Based
  • Item-Based

Collaborative Filtering

A movie recommender ML project with different approaches.

This repo contains several recommender systems I built while practicing. If you're practicing too, you can follow the roadmap I used it should make the journey smoother for you. The files are organized to help you understand different recommender system techniques and can be accessed easily for learning or reference. look through the notebooks and scripts to see how each method is implemented.

Feel free to contribute to this repo to make it broader, or open an issue with detailed information if you find something that's wrong or could be improved.

What's inside

• Multiple recommender implementations (collaborative filtering, content-based, hybrid, etc.)
• Notebooks and scripts for data processing, training, and evaluation
• Example datasets or instructions to download them
• A simple roadmap to guide practice and learning

Roadmap (how I practiced each topic)

1. First select one topic or category at a time (example, Cosine similarity from Content based recommendation systems)
2. Learn about the topic in detail including the math concept behind it and how you can implement it (You can watch different videos on YouTube to broaden your understanding in that area. some YouTube channels I recommend: 3Brown1Blue, Khan Academy, Aleason Person)
3. Explore dataset(s) and clean data
4. Build a baseline (e.g., popularity-based or simple averages)
5. Implementation
6. Evaluation and checking again

Use this as a guide to learn any ML algorithm. Adapt it to your data, goals, and interests.

Quick start

1. Clone the repo
2. Install requirements (see requirements.txt or the notebook)
3. Download or point to your dataset
4. Run the notebooks or scripts in the order in the roadmap

Contributing

Contributions are welcome! If you want to:

• Add a new approach or improvement, open a PR
• Suggest a change or report a bug, open an Issue and include steps to reproduce and any relevant data/notebook output
• Improve docs or add examples, PRs are appreciated

Issues

If something looks off, please open an Issue and provide as much detail as you can — expected vs actual behavior, dataset used, and code/notebook references help a lot.

---

Thanks for checking out Movie-Recommender — happy experimenting and learning!