---
title: Book Recommendation Predictor
emoji: 📚
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
---

# 📚 Book Recommendation Predictor

## Overview
An interactive machine learning application that predicts whether a book should be recommended to everyone based on its characteristics and reading experience. Built with AutoGluon's Gradient-Boosted Decision Tree (LightGBM) and deployed using Gradio.

## Model Details
- **Model Type**: Gradient-Boosted Decision Tree (LightGBM)
- **Framework**: AutoGluon-Tabular
- **Developer**: Zachary Zdobinski
- **Source Model**: [zacCMU/2024-24679-tabular-autolguon-predictor](https://huggingface.co/zacCMU/2024-24679-tabular-autolguon-predictor)
- **Task**: Binary Classification
- **Performance**: 55% Accuracy on test set

## Features

### Input Variables
1. **Fiction or Non-Fiction**: Genre classification of the book
2. **Number of Pages**: Total page count (1-2000 pages)
3. **Book Thickness**: Physical thickness in inches (0.1-5.0 inches)
4. **Reading Status**: Whether the book was Read, Unfinished, or Unread

### Output
- Binary prediction: "Recommended to Everyone" vs "Not Recommended to Everyone"
- Confidence scores for both classes
- Visual probability distribution

## How to Use

### Interactive Interface
1. **Select Book Genre**: Choose between Fiction or Non-Fiction
2. **Set Book Length**: Adjust the page count slider
3. **Specify Thickness**: Set the physical thickness or auto-calculate from pages
4. **Choose Reading Status**: Select whether you've read, not finished, or haven't read the book
5. **Make Prediction**: Click the prediction button to see results

### Example Inputs
The application includes 6 pre-configured examples demonstrating various book types:
- Popular fiction (completed)
- Long non-fiction (unfinished)
- Short books vs. long books
- Different reading completion statuses

## Technical Implementation

### Architecture
- **Frontend**: Gradio interface with interactive widgets
- **Backend**: AutoGluon TabularPredictor
- **Deployment**: Hugging Face Spaces
- **Model Format**: Compressed AutoGluon predictor directory

### Dependencies
- autogluon.tabular
- gradio
- huggingface_hub
- pandas

## Model Performance & Limitations

### Strengths
- Fast inference time
- Simple, interpretable features
- Handles both fiction and non-fiction genres
- Considers reading completion as a quality signal

### Limitations
- **Limited Feature Scope**: Only uses 4 features; doesn't consider author, writing style, or publication date
- **Cold Start Problem**: May perform poorly for users with minimal reading history
- **Genre Coverage**: Performance may vary for niche or underrepresented genres
- **Moderate Accuracy**: 55% accuracy indicates room for improvement

## Dataset Information
The model was trained on a private dataset containing:
- User reading history
- Book metadata (genre, length)
- Binary recommendation labels

## Use Cases
- **Personal Reading Lists**: Help readers identify broadly appealing books
- **Library Recommendations**: Assist librarians in selecting popular titles
- **Book Club Selection**: Find books likely to appeal to diverse groups
- **Publishing Insights**: Understand characteristics of universally appealing books

## Ethical Considerations
- Recommendations may reflect biases present in the training data
- The model should not be the sole factor in book selection decisions
- Consider diverse perspectives beyond algorithmic recommendations

## Future Improvements
- Incorporate additional features (author, publication year, user ratings)
- Improve model accuracy through ensemble methods
- Add multi-class recommendations for different audience segments
- Include content-based features from book descriptions

## Citation
If you use this model or interface, please cite:
@misc{book_recommendation_2025,
author = {Zachary Zdobinski},
title = {Book Recommendation Predictor},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/zacCMU/2024-24679-tabular-autolguon-predictor}
}

## Contact & Support
Created for CMU 24-679 Course Assignment
For questions or issues, please open an issue in the repository.

---
*Last Updated: September 2025*