A machine learning-powered book recommendation system utilizing metadata from thousands of books. This project leverages natural language processing (NLP) and modern AI techniques to suggest books based on user preferences.
We use a comprehensive dataset containing metadata for 7,000+ books, sourced from Kaggle: 7K Books with Metadata
To set up the project, ensure you have Python installed, then install the required dependencies:
pip install kagglehub numpy pandas matplotlib seaborn langchain-openai \
langchain-huggingface langchain-community langchain-chroma gradio \
transformers notebook ipywidgets- Data Processing:
numpy,pandas - Visualization:
matplotlib,seaborn - AI & NLP:
transformers(from Hugging Face),langchain-openai,langchain-gemini - Database for Storage:
langchain-chroma - Interactive UI:
gradio - Notebook Support:
notebook,ipywidgets - Frontend Interface:
Gradiofor displaying the project and interacting with recommendations - Data Processing:
numpy,pandas - Visualization:
matplotlib,seaborn - AI & NLP:
transformers(from Hugging Face),langchain-openai,langchain-gemini - Database for Storage:
langchain-chroma - Interactive UI:
gradio - Notebook Support:
notebook,ipywidgets
Word embeddings are numerical vector representations of words in a continuous vector space. They allow words with similar meanings to have similar representations, making them essential for NLP tasks. We use pre-trained transformer-based embeddings to convert book metadata into vector representations, enabling efficient similarity comparisons in our recommendation engine.
Popular word embedding techniques include:
- Word2Vec (Mikolov et al.)
- GloVe (Pennington et al.)
- BERT Embeddings (Context-aware)
For this project, we leverage transformer-based embeddings from Hugging Face, specifically the sentence-transformers/all-MiniLM model, to create high-quality vector representations of books.
Word embeddings are numerical vector representations of words in a continuous vector space. They allow words with similar meanings to have similar representations, making them essential for NLP tasks. We use pre-trained transformer-based embeddings to convert book metadata into vector representations, enabling efficient similarity comparisons in our recommendation engine.
Transformers are a deep learning architecture designed for NLP tasks. They use self-attention mechanisms to process text efficiently and capture contextual meaning. We utilize transformer-based models (like BERT, GPT, or OpenAI embeddings) to generate rich representations of book metadata, improving recommendation accuracy.
Key features of transformers:
- Self-Attention Mechanism: Helps models focus on important parts of text
- Bidirectional Context Understanding: Captures meaning from both left and right context
- Scalability: Suitable for large-scale NLP tasks
Since book categories can be vast and dynamic, we employ zero-shot classification using transformer models. This allows us to classify books into predefined genres without labeled training data. By using models like facebook/bart-large-mnli, we match book descriptions with relevant genres on-the-fly, making the system flexible and adaptive.
To enhance book recommendations, we incorporate sentiment analysis on user reviews. By analyzing emotions expressed in book reviews, we refine recommendations based on reader sentiment. We use a fine-tuned RoBERTa model from Hugging Face:
- Model:
j-hartmann/emotion-english-distilroberta-base - Purpose: Detect emotions in text (e.g., joy, sadness, anger, surprise, etc.)
- Impact: Helps recommend books that align with users' emotional preferences
- 📖 Personalized Book Recommendations based on metadata and AI-driven insights
- 🔍 Search and Filter Books using natural language queries
- 🎨 User-Friendly Interface powered by Gradio
- 📊 Data Visualization for book insights and trends
- 🔗 Integration with external book APIs for enriched recommendations
- 📈 Improving the recommendation engine using deep learning
- 🌐 Deploying as a web application for wider accessibility
Feel free to fork this repository, create a new branch, and submit a pull request with improvements or new features!
This project is open-source and available under the MIT License.
⭐ If you like this project, give it a star on GitHub!