🔥 BookSage AI

BookSage AI is a hybrid book recommendation system combining Collaborative Filtering (KNN-based) and Content-Based (TF-IDF + Cosine Similarity) models, with a weighted hybrid approach for personalized results. The project ingests and preprocesses large-scale book datasets, applies active-user and popular-book filtering, and dynamically generates recommendations enriched with metadata (title, author, publisher, year, and cover image). I engineered a modular architecture with clear data pipelines, model persistence, and reusable functions, ensuring scalability and maintainability. The system was deployed as a Dockerized Flask web application with a responsive HTML/CSS front-end, integrated into a CI/CD pipeline for automated builds and deployments. This design demonstrates proficiency in ML model building, orchestration, backend API development, front-end integration, containerization, and production-grade deployment workflows.

demo.mp4

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🎯 Project Goal

In today’s digital era, readers are overwhelmed with millions of book options across various platforms, making it difficult to find books aligned with their interests, preferences, or past reading history. Traditional search-based discovery methods fall short in providing personalized and engaging reading suggestions.

To address this challenge, the goal is to design and implement a personalized hybrid book recommendation system that leverages both collaborative filtering (based on user behavior and ratings) and content-based filtering (based on book metadata like author, publisher, and year). The system should support:

• Image-based output (for UI-friendly recommendations),
• A fallback mechanism for cold-start scenarios,
• Adjustable weights for hybrid scoring,
• Scalable performance for large datasets.

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🌐 Live Demo

🔗 Try the Hybrid Book Recommendation System live: 👉 https://booksage-ai.onrender.com/

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🧠 Core Technologies

Category	Technology / Resource
Core Language	Python 3.11
Backend Framework	Flask (REST API for recommendations)
Data Processing	Pandas (Data Cleaning & Merging), NumPy (Matrix Ops)
Recommendation Models	Hybrid System: Collaborative Filtering + Content-Based Filtering
Collaborative Filtering	SciPy (csr_matrix), scikit-learn (NearestNeighbors)
Content-Based Filtering	scikit-learn (TfidfVectorizer, cosine_similarity)
Hybrid Fusion Logic	Weighted average score combination
Data Sources	Book-Crossing Dataset (BX-Books, BX-Users, BX-Ratings)
Feature Engineering	TF-IDF on combined features (title, author, publisher, year)
Model Persistence	Pickle (Model & Processed Data Serialization)
Memory System	In-memory caching of processed data for faster responses
Evaluation Metrics	Popularity-based filtering, Active user filtering
Orchestration Layer	Modular service classes (DataLoader, DataPreprocessor, ModelManager, HybridModel)
Frontend	HTML5, Jinja2 Templates, Static CSS & JavaScript
Deployment	Docker (Python 3.11-slim base), requirements.txt dependency locking
Portability	Pathlib-based cross-platform directory resolution
Error Handling	Graceful fallbacks & empty results handling

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⚖️ Comparison with Standard Systems

Feature	BookSage AI	Typical Recommenders
Method Flexibility	✅ 3 modes + hybrid tuning	❌ Usually single-method
Cold Start Handling	✅ Popular books fallback	❌ Often fails
Explainability	✅ Shows scores + metadata	❌ Black-box results
UI Customization	✅ Adjustable weights/counts	❌ Fixed parameters

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📂 Project Structure for BookSage AI

BookSage-AI/
│
├── .github/
│   └── workflows/
│       └── main.yml
│
├── data/
│   ├── BX-Book-Ratings.csv
│   ├── BX-Books.csv
│   └── BX-Users.csv
│
├── main/
│   ├── __init__.py
│   ├── collaborative_model.py
│   ├── content_model.py
│   ├── config.py
│   ├── data_loader.py
│   ├── data_preprocessor.py
│   ├── hybrid_model.py
│   ├── main.py
│   ├── model_manager.py
│   └── recommendation_engine.py
│
├── models/
│   ├── book_pivot.pkl
│   ├── books_content.pkl
│   ├── books_data.pkl
│   ├── cb_model.pkl
│   ├── cf_model.pkl
│   ├── content_sim_matrix.pkl
│   ├── final_rating.pkl
│   ├── tfidf_vectorizer.pkl
│   └── title_to_idx.pkl
│
├── notebooks/
│   └── experiment.ipynb
│
├── static/
│   ├── css/
│   │   ├── style_recommender.css
│   │   └── style.css
│   └── js/
│       └── script.js
│
├── templates/
│   ├── index.html
│   └── recommendations.html
│
├── tests/
│   └── test_app.py
│
├── .gitignore
├── app.png
├── app.py
├── demo.mp4
├── Dockerfile
├── LICENSE
├── README.md
├── render.yml
├── requirements.txt
└── setup.py

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🧬 Architecture Diagram (Mermaid)

graph TD
    A[Raw Data: BX-Books, BX-Users, BX-Ratings] --> B[Data Preprocessing & Feature Engineering]
    B --> C[Collaborative Filtering Model]
    B --> D[Content-Based Model]
    
    C --> E[User-Item Matrix - csr_matrix]
    D --> F[TF-IDF Features - Title+Author+Publisher+Year]
    
    E --> G[Hybrid Recommender - Weighted Score Fusion]
    F --> G
    
    G --> H[Flask API - Endpoints for Recommendations]
    H --> I[Frontend: HTML + Jinja2 + CSS/JS]
    
    subgraph Deployment
        J[Docker Container]
        J --> H
        J --> I
    end
    
    G --> K[Model Persistence - Pickle Serialization]

Loading

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🛠️ How to Setup (Locally)

# Clone the Repository
git clone https://github.com/Md-Emon-Hasan/BookSage-AI.git
cd hybrid-book-recommender

# Setup Virtual Environment
python -m venv venv
source venv/bin/activate

# Install Dependencies
pip install -r requirements.txt

# Run Flask App
python app.py

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🐳 How to Setup (Dockerized)

# Clone the Repository
git clone https://github.com/Md-Emon-Hasan/BookSage-AI.git
cd BookSage-AI

# Build Docker Image
docker build -t booksage-ai .

# Run Docker Container
docker run -p 8501:8501 booksage-ai

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✍️ Prepared by:

Md Emon Hasan
📧 Email: [email protected]
💬 WhatsApp: +8801834363533
🔗 GitHub: Md-Emon-Hasan
🔗 LinkedIn: Md Emon Hasan
🔗 Facebook: Md Emon Hasan

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