Book Recommendation System

This is a book recommendation system built using Python. It utilizes a hybrid algorithm that combines content-based filtering and collaborative filtering techniques to provide personalized book recommendations to users. The system is implemented as a web application using Flask framework.

Table of Contents

• Book Recommendation System
  • Table of Contents
  • Installation
  • Usage
  • Features
  • Data
  • Dependencies
  • Contributing

Installation

1. Clone the repository:

git clone https://github.com/Niltopia/COMP7240Project.git

1. Install the required dependencies:

pip install -r requirements.txt

3. Set up the database:

• The system uses a SQLite database to store user information and ratings. Ensure that you have SQLite installed.
• Create a new SQLite database file database.db and import the provided database schema.
• Update the database file path in the connection() function in portal.py.

4. Train the recommendation models:

• Run the following commands to train the content-based and matrix factorization-based recommendation models:

python hybrid_algorithm.py

This will generate the required model files: tf_idf_model.pkl and mf_model.pkl.

5. Run the application:

python app.py

The application will be accessible at http://localhost:8081 by default.

Usage

• Register a new user account or log in with an existing account.
• Enter a book title or select a book from the list.
• Get personalized book recommendations based on your preferences and ratings.
• Rate a book to provide feedback and improve the recommendations.

Features

• Hybrid algorithm: The recommendation system combines content-based and collaborative filtering techniques for better personalized recommendations.
• User registration and authentication: Users can create new accounts and log in securely.
• Book search: Users can search for books by title and select a book from the list.
• Personalized recommendations: The system suggests books based on user preferences and ratings.
• Rating feedback: Users can rate books to provide feedback and improve the recommendation accuracy.

Data

The system uses a dataset of book information obtained from Goodreads. The dataset includes book titles, authors, descriptions, genres, and ratings.

Dependencies

The system relies on the following Python libraries:

• Flask: A micro web framework for building the web application.
• SQLite: A lightweight relational database used for storing user information and ratings.
• Surprise: A Python scikit for building and analyzing recommender systems.
• pandas: A data manipulation library for handling and processing the book dataset.
• numpy: A library for mathematical operations and computations.
• scikit-learn: A machine learning library used for feature extraction and similarity calculations.
• nltk: A natural language processing library for text preprocessing.

Contributing

Developer: Vesper - @LugiaIO - [email protected]

Developer: Henry Zheng - @FEI120483 - [email protected]