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---
title: Semantic Book Recommender
emoji: 📊
colorFrom: indigo
colorTo: yellow
sdk: gradio
sdk_version: 5.25.2
app_file: app.py
pinned: false
license: mit
short_description: A Semantic Book Recommendation System using LLM.
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
# Semantic Book Recommender
A semantic-based book recommendation system leveraging modern NLP techniques to provide context-aware suggestions.
## 🧠 Project Overview
This project explores the application of Natural Language Processing (NLP) and Large Language Models (LLMs) in building a semantic book recommender system. The system goes beyond traditional keyword-based recommendations by understanding the contextual meaning of book descriptions and user preferences.
## 📁 Project Structure
- **`data/`**: Contains the dataset used for analysis and model training.
- **`step_01_EDA.ipynb`**: Performs Exploratory Data Analysis to understand data distribution and key features.
- **`step_02_Vector_Search.ipynb`**: Implements vector-based search using sentence embeddings to find semantically similar books.
- **`step_03_Zero_Shot_Classification.ipynb`**: Applies zero-shot classification to categorize books without labeled data, utilizing pre-trained LLMs.
- **`step_04_Sentiment_Analysis.ipynb`**: Conducts sentiment analysis on book reviews to gauge reader opinions.
- **`step_05_Gradio_Dashboard.py`**: Develops an interactive dashboard using Gradio for users to input preferences and receive recommendations.
- **`requirements.txt`**: Lists all Python dependencies required to run the project.
## 🔍 Key Features
- **Semantic Search**: Utilizes sentence embeddings to capture the semantic meaning of book descriptions, enabling more accurate recommendations.
- **Zero-Shot Classification**: Employs pre-trained LLMs to classify books into genres or categories without the need for labeled training data.
- **Sentiment Analysis**: Analyzes user reviews to understand the general sentiment towards books, aiding in recommendation decisions.
- **Interactive Dashboard**: Provides a user-friendly interface for users to input their preferences and receive tailored book suggestions.
## 🚀 Getting Started
1. **Clone the repository**:
```bash
git clone https://github.com/YuITC/Semantic-Book-Recommender.git
cd Semantic-Book-Recommender
```
2. **Install dependencies**:
```bash
pip install -r requirements.txt
```
3. **Run the Gradio dashboard**:
```bash
python step_05_Gradio_Dashboard.py
```
## 📜 License
This project is licensed under the MIT License – feel free to modify and distribute it as needed.
## 🤝 Acknowledgments
If you find this project useful, consider ⭐️ starring the repository or contributing to further improvements!
## 📬 Contact
For any questions or collaboration opportunities, feel free to reach out:
📧 Email: [email protected]
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