Collaborative Filtering Recommendation Application

A movie recommendation system built using collaborative filtering techniques to provide personalized movie suggestions. The system analyzes user rating patterns and similarities between users to generate relevant recommendations.

• User-based collaborative filtering algorithm
• Cosine similarity for user preference matching
• Movie rating analysis and visualization
• Support for 1600+ movies and 900+ users
• Personalized recommendations based on user history

# Clone the repository
git clone https://github.com/yourusername/collaborative-recommender.git

# Change into project directory
cd collaborative-recommender

# Install required packages
pip install -r requirements.txt

pandas>=1.3.0
numpy>=1.21.0
scikit-learn>=0.24.0
streamlit>=1.0.0
plotly>=5.0.0

The system utilizes two main data files:

• Movie_data.csv: Contains user ratings and interactions

  • User_ID
  • User_Names
  • Movie_ID
  • Rating (1-5 scale)
  • Timestamp

• Movie_Id_Titles.csv: Movie metadata

  • Movie_ID
  • Movie_Title

Run the Streamlit web application:

streamlit run app.py

The interface allows you to:

1. Select a user from the dropdown menu
2. View rating distributions and statistics
3. Get personalized movie recommendations

1. Creates a user-movie interaction matrix
2. Computes user similarities using cosine similarity
3. Identifies most similar users for a given target user
4. Aggregates highly rated movies from similar users
5. Filters out previously watched movies
6. Returns top-N movie recommendations

collaborative-recommender/
├── CollaborativeFiltering.ipynb   # Core recommendation algorithm
├── app.py                         # Streamlit web interface
├── Movie_data.csv                 # User rating dataset
├── Movie_Id_Titles.csv           # Movie information
├── requirements.txt              # Package dependencies
└── README.md                     # Project documentation

• Number of Users: 944
• Number of Movies: 1,682
• Total Ratings: 100,003
• Matrix Sparsity: 6.3%

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.