Book Recommendation System

Welcome to the Book Recommendation System project! This system provides users with personalized book recommendations based on their preferences and interactions with the system. It also offers a curated list of top 50 books for users to explore.

About

This project aims to simplify the process of finding the perfect book to read by offering tailored recommendations. It utilizes collaborative filtering Recommendation System and content-based filtering techniques to suggest books similar to those liked by the user. Additionally, it provides a list of popular books based on user ratings and reviews using Popularity Based Recommendation System

Features

• Personalized book recommendations based on user input
• Top 50 books list display with ratings and reviews
• User-friendly interface with Bootstrap for responsiveness
• Easy setup and deployment with Flask

Technologies Used

• Spark and Hadoop
• Python
• Flask
• HTML
• CSS (Bootstrap)
• Pandas

Installation

1.Clone the repository to your local machine: git clone https://github.com/mk7562/Cloud-Computing.git

2.Navigate to the project directory: cd book-recommender

3.Install dependencies: pip install -r requirements.txt

Usage

1.Run the Flask application by executing the following command: python app.py

2.Access the application in your web browser at http://localhost:5000

3.Navigate to the Home page to view a list of top 50 books.

4.Navigate to the Recommend page to input a book title and receive recommendations based on the input.

Files

• app.py: Contains the Flask application logic.
• index.html: HTML template for the Home page.
• recommend.html: HTML template for the Recommend page.
• popular.pkl: Pickle file containing data of popular books.
• pt.pkl: Pickle file containing preprocessed data.
• books.pkl: Pickle file containing book data.
• similarity_scores.pkl: Pickle file containing similarity scores between books.

Usage

• Visit the homepage to explore the top 50 books or navigate to the recommendation page to receive personalized book suggestions.
• Enter your preferences or interests in the recommendation form and submit.
• Browse through the recommended books and click on any book to view more details.

Contributing

We welcome contributions from the community! If you have any suggestions, bug reports, or feature requests, please feel free to open an issue or submit a pull request.

Team Members

Manish Kumar(21bds036)

Ravi Ranjan(21bds057)