The Book Forum: Application System with Hybrid Filtering and Recommendation using Collaborative Filtering and Autoencoders

Ayushi Chawade, Kalpesh Khairnar, Vaishnavi Khamkar, Pratham Sonawane and Monali Bansode

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 3, 286-299

Abstract: The digital era has transformed the way we discover and engage with books. However, the abundance of available options often makes it challenging for users to find books tailored to their individual preferences. To address this issue, the development of Android applications incorporating advanced recommendation techniques has gained significant attention. This review paper explores the concept of using autoencoders with collaborative filtering and hybrid filtering algorithms as the backbone for book recommendation systems within Android applications. It leverages the power of autoencoders, a type of neural network, to generate personalized book recommendations based on user ratings, reading history, and behaviors. Collaborative filtering techniques analyze user interactions to identify patterns and similarities, while hybrid filtering combines multiple recommendation models to provide accurate and diverse suggestions. Additionally, the application offers features such as popular book recommendations and genre-based suggestions to cater to a wider range of user preferences. The application for book recommendations integrates advanced recommendation techniques using autoencoders and collaborative filtering. In addition to personalized recommendations, it offers a range of functionalities. Users can create wishlists, bookmark books, and even upload their own books as authors. An admin portal ensures efficient management and moderation of uploaded books. The application also includes a forum for user discussions about specific chapters or books. Overall, this comprehensive solution enhances user satisfaction and engagement, providing a seamless reading experience. This review paper highlights the innovative idea and its implications for the book industry and digital reading community.

Keywords: Book Recommendation; Collaborative Filtering; Deep Neural Network Architecture; Context Aware Component (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT2390381
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/CSEIT2390381 Article URL (text/html)
https://ijsrcseit.com/paper/CSEIT2390381.pdf Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v9:y2023:i3:id:hcseit2390381

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().