Online Book Recommendation System

ABSTRACT:

In today’s digital era, online social networks play a crucial role in shaping user preferences and habits. One of the most productive habits that can be cultivated through these networks is reading. The Online Book Recommendation System is designed to encourage users to engage in reading by recommending books within an online social network. The system is developed using Java as the programming language, with JSP, CSS, and JavaScript for the frontend and MySQL as the database. This system leverages user interactions, ratings, and recommendations to provide a personalized book selection experience, fostering a reading culture among users.

The need for an Online Book Recommendation System arises from the challenge of discovering relevant books based on user preferences. Traditional book recommendation systems often rely on automated algorithms, but they lack a social dimension where users can actively share and recommend books to their friends.

By integrating a recommendation system within a social networking platform, this project enhances user engagement, making book discovery more interactive and personalized. The system allows users to rate books, recommend them to friends, and receive recommendations from others, thus creating a dynamic and engaging reading environment.

By integrating book recommendations within an online social network, this system provides a user-friendly platform to explore, share, and discuss books. The combination of social connectivity and personalized suggestions makes it an innovative approach to promoting reading habits among users.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

The existing systems for book recommendations primarily relied on traditional search methods and standalone recommendation algorithms. These systems were typically implemented in library management software, e-commerce platforms, or dedicated book recommendation websites, where users could search for books based on title, author, or genre. Some systems integrated collaborative filtering and content-based filtering techniques to suggest books based on user preferences and past interactions.
In the existing system, book enthusiasts relied predominantly on traditional methods for discovering new literature, primarily through word-of-mouth recommendations, bookstore visits, or browsing through catalogues. This approach often resulted in limited exposure to diverse genres and authors, as well as a lack of personalized recommendations tailored to individual preferences.
Online bookstores and libraries provided a partial solution, offering users the ability to browse through extensive collections and access reviews and ratings. However, these platforms lacked the interactive and social elements necessary to foster a sense of community and facilitate meaningful discussions around literature.
In the existing system, users could browse through catalogs and check the availability of books. Similarly, online book-selling platforms provided recommendations based on purchase history, popular trends, and manually curated book lists. These systems aimed to help users discover books, but they lacked an interactive social component where users could engage with friends and share recommendations directly.
Another approach in existing systems included review-based recommendations, where users could read and provide reviews for books. Platforms like Good reads and Amazon utilized star ratings and textual reviews to offer book suggestions to other readers. These platforms enhanced user engagement by allowing discussions in community forums and book groups.
Overall, the existing systems provided users with book search and recommendation functionalities but were often limited to individual interactions rather than fostering a socially connected book discovery experience.

DISADVANTAGES OF EXISTING SYSTEM:

Limited Exposure: The existing systems relied heavily on traditional methods such as word-of-mouth recommendations and bookstore browsing, limiting users’ exposure to a narrow range of books and genres. This resulted in a lack of diversity in reading choices and missed opportunities to discover lesser-known authors and titles.
Lack of Social Interaction: The existing system recommendation systems operated in isolation, where users could search for books and view recommendations based on algorithms but had no option to interact with friends or share book suggestions within a social network.
Limited Personalization: Many existing systems relied solely on predefined categories, genre-based filtering, or purchase history for recommendations. This approach often led to generic suggestions that did not align with a user’s specific interests or evolving reading preferences.
Absence of User-Generated Recommendations: Users could rate and review books, but there was no way to recommend books directly to friends. Without a user-driven recommendation feature, book discovery remained passive rather than interactive.
Dependency on Static Data: Older systems mostly used static datasets or predefined recommendation rules. They lacked dynamic updates based on real-time user interactions, making them less adaptive to changing trends and preferences.
No Friend Connectivity: The existing systems did not allow users to connect with friends, send friend requests, or engage in discussions about books. This limited the sense of community among readers.
Lack of Visual Insights: Most of the existing book recommendation systems did not provide visual representations of user ratings and preferences. There were no graphical analytics, such as bar charts or trend graphs, to help users and administrators understand book popularity.
Difficulty in Discovering Niche Books: Recommendation systems often focused on bestsellers and popular books, making it difficult for users to discover niche or lesser-known books based on personalized suggestions.
Due to these limitations, there was a need for a more engaging and interactive system that combines social networking with book recommendations, allowing users to share their reading experiences and get personalized suggestions from their friends.

PROPOSED SYSTEM:

The Online Book Recommendation System is designed to integrate book discovery with social networking, allowing users to share and receive book recommendations from their friends. The system is developed using Java as the programming language, with JSP, CSS, and JavaScript for the frontend and MySQL as the database. It consists of two main entities: Users and Admin.
Users have access to features such as registration, login, profile management, friend requests, book search, rating, and recommendations. After logging in, users can connect with friends, send and receive book recommendations, and explore books using an ISBN-based search. The recommendation system allows users to suggest books to their friends along with personalized comments, making the process interactive and engaging. Users can also view the recommendations received from their friends in a structured format.
The Admin module provides functionalities to manage users, books, and ratings. Admins can view user details, add new books, monitor book ratings, and generate graphical representations of rating trends using bar charts. This feature helps analyze book popularity based on user feedback, ensuring a more refined and data-driven recommendation experience.
The recommendation system allows users to select books using their ISBN numbers, view book details such as author, publisher, and year of publication, and provide ratings. The friend request system ensures that recommendations are exchanged within a trusted network, making the book discovery process more engaging.
By combining book recommendations with social networking features, this system creates an interactive platform that encourages users to explore new books based on trusted suggestions from their friends.

ADVANTAGES OF PROPOSED SYSTEM:

Personalized Book Recommendations: In the proposed system, Users receive book suggestions from their friends rather than generic algorithm based recommendations, making the process more relevant and tailored to their interests.
Enhanced Social Connectivity: The proposed system enables users to connect with friends through friend requests, view their recommendations, and share reading experiences, fostering an interactive and engaging reading community.
Improved User Engagement: Unlike earlier systems that only provided book ratings and reviews, this platform actively involves users in recommending books, viewing recommendations, and commenting, increasing user participation.
Easy Book Discovery: Users can search for books using the ISBN number and quickly access essential details such as the title, author, publisher, and year of publication, simplifying the book selection process.
Interactive and User-Friendly Interface: Developed using JSP, CSS, and JavaScript, the frontend provides an intuitive and visually appealing interface that enhances user experience.
Accurate Book Rating System: The system allows users to rate books, helping others make informed decisions based on community feedback. These ratings are also stored and managed efficiently in the MySQL database.
Admin Control and Management: The Admin Panel provides comprehensive management features, including user and book management, rating monitoring, and adding new books to the system, ensuring smooth operation.
Graphical Representation of Book Ratings: The proposed system includes dynamic graphs that display book ratings in a bar chart format, allowing both users and administrators to analyze book popularity trends visually.
Trust-Based Recommendations: Since book recommendations come from friends, users are more likely to trust and explore new books, making the reading experience more engaging and reliable.
Encourages Reading Habits: By integrating book recommendations into an online social network, the system encourages users to develop and maintain a habit of reading, ultimately promoting literacy and knowledge sharing.

By leveraging these advantages, the Online Book Recommendation System enhances book discovery, improves social interaction among readers, and provides an efficient and enjoyable reading experience.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 20 GB.
Monitor : 15’’ LED.
Input Devices : Keyboard, Mouse.
Ram : 4 GB.

SOFTWARE REQUIREMENTS:

Operating system : Windows 10/11.
Coding Language : Java.
Frontend : JSP, CSS, JavaScript.
JDK Version : JDK 23.0.1.
IDE Tool : Apache Netbeans IDE 24.
Tomcat Server Version : Apache Tomcat 9.0.84
Database : MYSQL.

Java Projects

Online Book Recommendation System