Book Recommender System

Welcome to the Book Recommender System repository! This project aims to provide book recommendations to users based on their preferences and historical interactions. Whether you are an avid reader or just looking for your next great read, this system can help you discover books that align with your interests.

Table of Contents

• Introduction
• How It Works
• Installation
• Usage
• Data
• Technologies Used
• Contributing
• License

Introduction

The Book Recommender System is built using collaborative filtering techniques that analyze user behaviour and patterns to recommend books. It leverages data on user interactions with books, such as ratings and reviews, to make personalized suggestions. The goal is to provide users with tailored book recommendations that match their tastes and preferences, leading to a more satisfying reading experience.

How It Works

The recommender system is built on a collaborative filtering algorithm, which has two main components:

1. User-User Collaborative Filtering: This method recommends books based on the preferences of similar users. It identifies users who have similar reading habits, and if a user has read or liked books that another similar user has enjoyed, the system will recommend those unread books to the first user.

2. Item-Item Collaborative Filtering: This approach recommends books by finding similarities between items (books). It identifies books that are similar to the ones a user has already read or liked. If a user has enjoyed certain books, the system will suggest other books that share similar characteristics or are commonly liked by users who have also enjoyed the original books.

Installation

To use the Book Recommender System on your local machine, follow these steps:

1. Clone this repository to your local machine using the following command:

git clone https://github.com/Dishantkharkar/Book_Recommendation_System.git

2. Change into the project directory:

cd Book_Recommendation_System

3. Install the required dependencies. It is recommended to set up a virtual environment before installing the dependencies.

pip install -r requirements.txt

Usage

To use the Book Recommender System, you need to provide the necessary data in the required format. The data should include information on user interactions with books, such as user IDs, book IDs, ratings, and reviews. You can either use your own data or explore publicly available datasets related to books and user interactions.

Once you have the data, follow these steps:

1. Preprocess the data: Prepare the data in a format suitable for the recommender system. The data should be organized in a way that captures user-item interactions.

2. Train the model: Use the provided scripts to train the collaborative filtering algorithm on your preprocessed data.

3. Get recommendations: Once the model is trained, you can obtain book recommendations for specific users by running the relevant scripts.

4. Evaluate the system: Measure the performance of the Book Recommender System using appropriate evaluation metrics to assess its effectiveness.

Data

The repository does not include any specific dataset. You can use your own data or explore publicly available datasets related to books and user interactions. Make sure to format the data properly for the recommender system to work effectively. Dataset Link - https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset

Technologies Used

The Book Recommender System is built using the following technologies:

• Python: The core programming language used for developing the system.
• Collaborative Filtering: The recommendation algorithm used to provide personalized book suggestions.
• Pandas: Used for data manipulation and preprocessing.
• NumPy: Utilized for numerical computations.
• Scikit-learn: Employed for machine learning and evaluation.
• Matplotlib: Used for data visualization.

Contributing

We welcome contributions to enhance the Book Recommender System. If you find any bugs or have ideas for improvements, please feel free to open an issue or submit a pull request. Your contributions are highly appreciated!

License

The Book Recommender System is open-source and available under the [MIT License]

Happy reading and discovering new books with the Book Recommender System! If you have any questions or need assistance, feel free to reach out.