Book Recommendation System

Table of Contents

• Overview
• Technologies Used
• Features
• Installation
• Project File Structure

Overview

The Book Recommendation System is a collaborative filter-based recommendation engine that suggests similar books to users based on their search history and preferences. By leveraging a comprehensive books dataset, this system provides personalized book recommendations to enhance user experience and discovery.

Technologies Used

• Python: For implementing recommendation algorithms and data processing.
• Pickle: For serialization and deserialization of the recommendation model.
• Flask: For developing the web application interface.

Features

• Collaborative Filtering: Utilizes collaborative filtering techniques to recommend books similar to those the user has shown interest in.
• Book Dataset: Uses a dataset that includes book titles, authors, genres, and user ratings to generate recommendations.
• Web Application Interface: Built with Flask, providing an interactive user interface for searching and receiving book recommendations.
• Model Serialization: Implements Pickle to save and load the recommendation model efficiently.

Installation

To set up the project locally, follow these steps:

1. Create and Activate a Virtual Environment:

   • On Linux/macOS:

   python3 -m venv virtual_env
   source virtual_env/bin/activate

   • On Windows:

   python -m venv virtual_env
   virtual_env\Scripts\activate
   

2. Create and Navigate to the Project Directory:

   mkdir book-recommendation-system
   cd book-recommendation-system

3. Clone the Repository:

   git clone https://github.com/Raj-Dusane/Book-Recommendation-System.git

4. Install Required Dependencies:

   pip install -r requirements.txt

5. Run the Flask Application:

   python app.py

   The application will be accessible at 'http://127.0.0.1:5000'.

Project File Structure

• /dataset: Directory contaning raw dataset.

• /webapp: Directory structure for web app.

  • /models: Directory for serialized recommendation model.

    Serialized models:

    1. books.pkl: Contains a DataFrame with book details.
    2. popular.pkl: Provides recommendations for the top 50 books based on user ratings.
    3. pt.pkl: Holds the pivot table mapping books to user ratings.
    4. similarity_score.pkl: Stores the similarity scores between books.

  • /templates: Standard directory for HTML templates used by Flask.

    • index.html: Main page.
    • recommend.html: Renders the recommendations based on user search input.

• app.py: The main Flask application file.

• requirements.txt: List of required Python packages.

• Based_Book_Recommendation_System.ipynb: Illustrates the data flow and the operation of the recommendation model.