---
title: Book Recommender System
emoji: 🏆
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.35.0
app_file: app.py
pinned: false
---


# Hybrid Book Recommender System

This is a **Hybrid Recommendation System** deployed on Hugging Face Spaces using **Gradio**.  
It combines **Collaborative Filtering (SVD++)** and **Content-Based Filtering** to recommend books to users, even if they are new to the system (cold-start support).  

🔗 **Live Demo**: [Check it out on Hugging Face Spaces](https://huggingface.co/spaces/khalednabawi11/Book-Recommender-System)  

---

## Features

- **Hybrid Model**: Combines collaborative filtering and content-based filtering for more accurate recommendations.  
- **Cold Start Ready**: Recommends top-rated books to new users with no prior ratings.  
- **Book Covers**: Shows book covers alongside titles for a more visual experience.  
- **Gradio**: Backend runs on FastAPI, frontend built with Gradio for an interactive experience.  
- Model loaded from Kaggle Models using `kagglehub`.  

---

## How It Works

1. **For registered users**:
   - Predicts ratings for books the user hasn’t rated yet.
   - Shows the top N books sorted by predicted rating.

2. **For new users**:
   - Recommends globally top-rated books as a fallback (cold start).  

3. **Visual Output**:
   - Book title, predicted rating, and cover image in a neat table.

---

---
## Project Structure

- ├── app.py # Main Gradio app
- ├── requirements.txt # Dependencies
- ├── README.md
- ├── books-recomendation-system-0.ipynb => Model Development Notebook
---




## Setup Locally

Clone the repo and run:  

```bash
pip install -r requirements.txt
python app.py
```

##  Dependencies

```
- gradio
- pandas
- numpy
- scikit-learn
- kagglehub
- surprise (for SVD++ model)
- fastapi
- uvicorn
```

## Model Details

- Collaborative Filtering: SVD++ trained on user-item ratings.
- Content-Based Filtering: Uses book metadata (title, genres, etc.).
- Hybrid approach balances both for better personalization.


## Deployment
```
- This app is deployed on Hugging Face Spaces using Gradio UI and loads the model directly from Kaggle Models via kagglehub.
```



Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference