A sophisticated book recommendation system built with Python that uses Natural Language Processing (NLP) and Machine Learning techniques to provide personalized book recommendations based on content similarity.
- Text preprocessing using NLTK for enhanced accuracy
- TF-IDF vectorization for feature extraction
- Cosine similarity-based recommendation generation
- Model persistence capabilities
- Memory-efficient processing using garbage collection
- Customizable number of recommendations
python >= 3.7
pandas
numpy
scikit-learn
nltk- Clone the repository:
git clone https://github.com/yourusername/book-recommender.git
cd book-recommender- Install required packages:
pip install -r requirements.txt- Download required NLTK data:
import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')- Basic usage:
from book_recommender import BookRecommender
# Load your dataset
df = pd.read_csv('path/to/your/books.csv')
# Initialize and train the recommender
recommender = BookRecommender()
recommender.fit(df)
# Get recommendations
recommendations = recommender.get_recommendations("The Hobbit")- Save and load the model:
# Save the model
recommender.save_model('./ml_model/book_recommender.pkl')
# Load a saved model
loaded_recommender = BookRecommender.load_model('./ml_model/book_recommender.pkl')Your dataset should include the following columns:
Title: Book titleAuthors: Book author(s)Description: Book description/summaryCategory: Book category/genre
recommendations = recommender.get_recommendations("The Hobbit")
# Returns:
[
{
'Title': 'The Lord of the Rings',
'Author': 'J.R.R. Tolkien',
'Category': 'Fantasy',
'Similarity Score': 0.876
},
# ... more recommendations
]-
Text Preprocessing:
- Converts text to lowercase
- Removes numbers and punctuation
- Eliminates stop words
- Applies lemmatization
-
Feature Extraction:
- Uses TF-IDF vectorization
- Processes title, description, and category information
- Creates a feature matrix for similarity computation
-
Recommendation Generation:
- Computes cosine similarity between books
- Ranks similar books by similarity score
- Returns top N recommendations
You can customize the following parameters:
- Number of recommendations (default: 5)
- Maximum features for TF-IDF vectorization (default: 5000)
- Text preprocessing options in the
_clean_textmethod
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- NLTK for natural language processing capabilities
- scikit-learn for machine learning functionality
- pandas and numpy for data manipulation
- Add collaborative filtering
- Implement user feedback system
- Add support for multiple languages
- Enhance preprocessing techniques
- Add API endpoints for web service integration