ACSAIS Favorite Books

Interactive network visualization showing relationships between books and users

A book recommendation application that allows users to vote on books, view community favorites, and get personalized recommendations. This project combines a modern Next.js frontend with Python-based data analysis and uses Supabase for data storage.

🚀 Features

• 📊 Book Voting: Vote on your favorite books
• ❤️ Community Favorites: View the most popular books among ACSAIS members
• 🕸️ Recommendations: Get personalized book recommendations based on community data
• 📈 Data Analysis: Python-based analytics for book trends and user preferences
• 🎨 Modern UI: Built with Next.js, TypeScript, and Tailwind CSS

Book recommendations interface showing personalized suggestions based on class votes using vector dataset matching for users with similar tastes

🛠️ Tech Stack

Frontend

• Next.js 14 - React framework for production
• TypeScript - Type-safe JavaScript
• Tailwind CSS - Utility-first CSS framework
• Nivo - Data visualization components

Backend & Database

• Supabase - Backend-as-a-Service with PostgreSQL
• Python - Data analysis and processing
• R - Statistical analysis and visualization

Key Dependencies

• @supabase/supabase-js - Supabase client for JavaScript
• @nivo/network - Network graph visualizations
• python-dotenv - Environment variable management
• vecs - Vector similarity search
• psycopg2-binary - PostgreSQL adapter for Python

📦 Installation

Prerequisites

• Node.js 18+ and npm/pnpm
• Python 3.8+
• R (for data analysis)

Frontend Setup

1. Clone the repository

   git clone <repository-url>
   cd BookRecommendation

2. Install Node.js dependencies

   npm install
   # or
   pnpm install

3. Set up environment variables Create a .env.local file with your Supabase credentials:

   NEXT_PUBLIC_SUPABASE_URL=your-supabase-url
   NEXT_PUBLIC_SUPABASE_ANON_KEY=your-supabase-anon-key

4. Run the development server

   npm run dev
   # or
   pnpm dev

   Open http://localhost:3000 to view the application.

Python Environment Setup

1. Create a virtual environment

   chmod +x setup_venv.sh
   ./setup_venv.sh

2. Install Python dependencies

   pip install -r requirements.txt

📁 Project Structure

BookRecommendation/
├── src/
│   ├── app/                 # Next.js app directory
│   │   ├── vote/           # Book voting page
│   │   ├── favs/           # Community favorites page
│   │   └── recs/           # Recommendations page
│   └── components/         # Reusable React components
├── data_analysis/          # Python & R analysis scripts
│   ├── bipartite.py       # Network analysis
│   ├── book_analysis.R    # Statistical analysis
│   └── presentation.txt   # Analysis results
├── utils/                  # Utility functions
├── public/                 # Static assets
├── plots/                  # Generated visualizations
└── presentation/           # Project presentation files

🚀 Usage

1. Vote on Books: Navigate to /vote to rate and vote on books
2. View Favorites: Check out /favs to see the community's most loved books
3. Get Recommendations: Visit /recs for personalized book suggestions based on community preferences

📊 Data Analysis

The project includes comprehensive data analysis capabilities:

• Network Analysis: Bipartite graph analysis of user-book relationships
• Statistical Analysis: R-based statistical modeling of reading preferences
• Visualization: Interactive charts and network graphs using Nivo

🔧 Available Scripts

Frontend

• npm run dev - Start development server
• npm run build - Build for production
• npm run start - Start production server
• npm run lint - Run ESLint

Data Analysis

• Run Python scripts in data_analysis/ for book recommendation algorithms
• Execute R scripts for statistical analysis and visualization

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

🔗 Links

• Next.js Documentation
• Supabase Documentation
• Tailwind CSS Documentation