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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: tainguyenphu2502@gmail.com | |