A book recommendation system is an intelligent application that suggests books to users based on their interests or preferences. It plays a vital role in platforms like Amazon, Goodreads, or Kindle, where personalized recommendations can enhance user experience and increase engagement.
There are several types of recommendation systems:
- Collaborative Filtering: Based on user behavior and preferences.
- Content-Based Filtering: Based on book metadata (title, author, genre, etc.)
- Hybrid Systems: Combine both techniques.
In this project, we’ve used Collaborative-Based Filtering, which works by:
- Extracting features (like title, author, tags, and description) from the book dataset.
- Using TF-IDF Vectorization to convert text into numerical format.
- Computing cosine similarity between books to find the most similar titles.
- Recommending top N books that are closest in similarity to the selected book.
This approach is fast and doesn’t require user interaction history, making it ideal for new users or small-scale systems.
- Recommend books based on a selected title
- User-friendly web interface
- Trained model using content-based filtering
- Fast and responsive UI with dropdown search
- Deployed using Render
- Python
- Flask
- Pandas / NumPy
- Scikit-learn
- Pickle (for model serialization)
- HTML / CSS (frontend)
- Render (for deployment)
🌐 Live Demo Check it out here: https://books-recommendation-system-01tc.onrender.com