This is CineDeepMatch (CDM), a real-time user-preference-oriented movie-recommendation system that runs asynchronously in seconds with the user's command.
This GitHub is dedicated to a research project called: "CineDeepMatch: Harnessing Mathematics for a Sequential Model for Amplifying Temporal User Preferences."
- About Us
- Project Overview
- Features
- Useful Resources
- Fun fact
- Technology Stack
- Getting Started
- Model Architecture
- Evaluation
- Contributing
- License
- Acknowledgements
- Join us
- Contact
We are at the forefront of creating a revolutionary movie recommendation experience, blending cutting-edge technology with a deep passion for cinema. Our team is pioneering the development of a graph-based attention model to intuitively connect users with movies, ensuring every recommendation is a gateway to a new adventure.
Our project is an ambitious endeavor to redefine the landscape of movie recommendations. Here's what sets us apart:
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Personalized Experience: At the heart of our system lies a graph-based attention model, designed to analyze complex user-movie interactions and deliver personalized recommendations that resonate with individual preferences and undiscovered tastes.
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Innovative Technology: We leverage the latest advancements in machine learning, graph theory, and data analytics to understand nuances in user behavior and movie attributes. Our model dynamically adjusts to evolving preferences, ensuring a consistently relevant and engaging user experience.
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Scalable Architecture: Built with scalability in mind, our system is capable of accommodating an ever-growing user base and movie database without compromising on performance.
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Community-Driven Approach: We believe in the power of community collaboration to drive innovation. Our project is not just a platform but a growing ecosystem where movie enthusiasts and tech visionaries come together.
This project combines graph neural networks with attention mechanisms to analyze user preferences and movie characteristics in unprecedented detail. Our goal is to create a scalable, efficient, and highly accurate movie recommendation system that adapts to individual tastes and evolving trends.
- Personalized Movie Recommendations: Tailor suggestions to individual user preferences and interaction histories.
- Graph-Based Attention Model: Utilize advanced graph neural networks and attention mechanisms to interpret complex user-movie relationships.
- Scalable and Efficient: Designed for performance, handling large-scale data with minimal latency.
- Dynamic User Profiling: Continuously update user profiles to reflect changing preferences and interaction patterns.
- Get Started: Familiarize yourself with our system by diving into our Documentation.
- Community Discussions: Share your ideas, ask questions, or connect with fellow contributors on our Discussions page.
Did you know our team's favorite movie genre is Sci-Fi? Perhaps that's why we're always excited about using futuristic technology to solve age-old questions of "What should I watch next?"
Together, let's transform movie watching into an endlessly exciting journey of discovery. Thank you for being part of this adventure! 🌟
- Programming Languages: Python
- Machine Learning Frameworks: PyTorch, TensorFlow
- Graph Processing: DGL, Neo4j
- Frontend: ReactJS (for demonstration purposes)
- Backend: Flask/Django
- Database: PostgreSQL, Redis (for caching)
- Python 3.8+
- Node.js 12+ (for ReactJS frontend)
- Docker (optional for containerization)
Clone the repository and set up a virtual environment:
git clone https://github.com/<your-username>/advanced-movie-recommendation.git
cd advanced-movie-recommendation
python -m venv venv
To run the application:
python app.py
For Docker users:
docker-compose up --build
Our Graph-Based Attention Model (GBAM) intricately analyses user-movie interactions through:
- Graph Construction: Nodes are users and movies; edges are interactions.
- Feature Extraction: Embeddings for user and movie attributes.
- Attention Mechanism: Node and graph-level attention for relevance.
- Recommendation Engine: Combines embeddings to forecast preferences. This architecture enables dynamic, personalized recommendations.
We assess our model using:
- Precision@K: Accuracy of top-K recommendations.
- Recall@K: Coverage of relevant items in top-K recommendations.
- NDCG: Ranking quality, factoring the position of relevant items. Continuous evaluation ensures the system's effectiveness.
Your expertise can help us enhance the magic of movie discovery. Whether you're a coder, a designer, or a movie buff with ideas, there's a place for you here. Check out our Contribution Guidelines for detailed information on how you can make an impact. Join us in enhancing movie recommendations:
Fork the project.
Create your feature branch (git checkout -b feature/AmazingFeature).
Commit your changes (git commit -m 'Add some AmazingFeature').
Push to the branch (git push origin feature/AmazingFeature).
Open a Pull Request.
Read our Contribution Guidelines for more details.
This project is licensed under the MIT License - see the LICENSE file for details.
Thanks to:
TMDB and GroupLens for dataset provision. The open-source community for essential tools and libraries. Contributors for their invaluable input and feedback. Your support is crucial to our project's success.
Dive into our open issues or kickstart a discussion. Your contribution could be the next big thing in movie recommendations!
Got questions or suggestions? We’d love to hear from you. Reach out to us at [letuanminh2707@gmail.com].