📚 Books Recommender System

A collaborative filtering-based book recommendation system built with Python and Streamlit. This application uses K-Nearest Neighbors (KNN) algorithm to suggest books similar to a user's selection based on user ratings data.

🔗 Live Demo: https://books-recommender-system-vineet416.streamlit.app/

🎯 Features

• Collaborative Filtering: Recommendations based on user rating patterns
• Interactive UI: Clean and intuitive Streamlit interface
• Visual Recommendations: Displays book covers for recommended titles
• Real-time Suggestions: Get 5 book recommendations instantly
• Large Dataset: Trained on Book-Crossing dataset with thousands of books and ratings

🛠️ Technologies Used

• Python 3.8+
• Streamlit - Web application framework
• NumPy - Numerical computing
• Pandas - Data manipulation and analysis
• Scikit-learn - Machine learning (KNN algorithm)
• Pickle - Model serialization

📁 Project Structure

Books Recommender Project/
│
├── app.py                      # Main Streamlit application
├── research.ipynb              # Jupyter notebook for EDA and model training
├── requirements.txt            # Project dependencies
│
├── artifacts/                  # Serialized models and data
│   ├── model.pkl              # Trained KNN model
│   ├── book_names.pkl         # List of book titles
│   ├── book_pivot.pkl         # Pivot table of user-book ratings
│   └── final_rating.pkl       # Processed ratings with metadata
│
├── Datasets/                   # Raw datasets (Book-Crossing)
│   ├── BX-Books.csv           # Book information
│   ├── BX-Users.csv           # User information
│   └── BX-Book-Ratings.csv    # User ratings
│
└── env/                        # Virtual environment (not tracked in git)

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Clone the repository

   git clone https://github.com/vineet416/Books_Recommender_System.git
   cd "Books Recommender Project"

2. Create a virtual environment (recommended)

   conda create -n env python=3.11
   conda activate env/

3. Install dependencies

   pip install -r requirements.txt

4. Ensure artifacts folder exists Make sure the artifacts/ folder contains all the required pickle files:

   • model.pkl
   • book_names.pkl
   • book_pivot.pkl
   • final_rating.pkl

Running the Application

1. Start the Streamlit app

   streamlit run app.py

2. Open your browser The application will automatically open in your default browser at http://localhost:8501

3. Use the application

   • Select a book from the dropdown menu
   • Click "Show Recommendation"
   • View 5 similar book recommendations with cover images

📊 How It Works

Collaborative Filtering Approach

1. Data Preprocessing:

   • Loads Book-Crossing dataset containing books, users, and ratings
   • Filters users with significant rating history
   • Filters popular books with sufficient ratings
   • Creates a user-book rating matrix

2. Model Training:

   • Uses K-Nearest Neighbors (KNN) algorithm
   • Calculates similarity between books based on user rating patterns
   • Books rated similarly by the same users are considered similar

3. Recommendation Generation:

   • Takes a selected book as input
   • Finds the 6 nearest neighbors (including the input book)
   • Returns 5 most similar books with cover images

Algorithm Details

• Algorithm: K-Nearest Neighbors (KNN)
• Similarity Metric: Cosine similarity / Euclidean distance
• Number of Neighbors: 6 (returns 5 recommendations)
• Approach: User-Item Collaborative Filtering

📈 Dataset

The project uses the Book-Crossing Dataset, which contains:

• Books: 271,360 books with metadata (ISBN, title, author, publisher, etc.)
• Users: 278,858 users (anonymized)
• Ratings: 1,149,780 ratings (scale 0-10)

Dataset Source: Book Recommendation Dataset on Kaggle

Original Source: Book-Crossing dataset collected by Cai-Nicolas Ziegler

🔧 Model Training

To retrain the model or explore the data analysis process:

1. Open research.ipynb in Jupyter Notebook or VS Code
2. Run all cells sequentially
3. The notebook will:
   • Load and explore the datasets
   • Perform data cleaning and preprocessing
   • Create the user-item matrix
   • Train the KNN model
   • Save artifacts to the artifacts/ folder

📝 Key Functions

recommend_book(book_name)

• Input: Book title (string)
• Output: List of recommended books and their poster URLs
• Process: Finds nearest neighbors using KNN model

fetch_poster(suggestion)

• Input: Book indices from KNN suggestions
• Output: List of poster/cover image URLs
• Process: Maps book indices to image URLs from dataset

🎨 User Interface

The Streamlit interface includes:

• Clean header with emoji icon
• Descriptive subtitle and methodology info
• Searchable dropdown for book selection
• Recommendation button
• 5-column grid layout for displaying results
• Book titles and cover images for each recommendation
• Error handling with user-friendly messages

🤝 Contributing

Contributions are welcome! Here are some ways you can contribute:

• Report bugs and issues
• Suggest new features or improvements
• Improve documentation
• Add new recommendation algorithms
• Enhance the UI/UX

📄 License

This project is open-source and available for educational purposes.

🙏 Acknowledgments

• Book-Crossing Dataset - For providing the comprehensive books and ratings data
• Streamlit - For the excellent web framework
• Scikit-learn - For machine learning tools

� Author

Vineet Patel

• �📧 Email: [email protected]
• 💻 GitHub: @vineet416
• 🔗 LinkedIn: @vineet416

📧 Contact

For questions or feedback, please open an issue in the repository or reach out via email.

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Note: Make sure to have all pickle files generated before running the app. Run the research.ipynb notebook first to generate these files if they don't exist.

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