A Comparison of Similarity Measures in an Online Book Recommendation System
subject: Books; Collaborative filtering; Machine learning; Recommendation system; Similarity; User
description: To assist users in identifying the right book, recommendation systems are crucial to e-commerce websites. Methodologies that recommend data can lead to the collection of irrelevant data, thus losing the ability to attract users and complete their work in a swift and consistent manner. Using the proposed method, information can be used to offer useful information to the user to help enable him or her to make informed decisions. Training, feedback, management, reporting, and configuration are all included. Our research evaluated user-based collaborative filtering (UBCF) and estimated the performance of similarity measures (distance) in recommending books, music, and goods. Several years have passed since recommendation systems were first developed. Many people struggle with figuring out what book to read next. When students do not have a solid understanding of a topic, it can be difficult determining which textbook or reference they should read. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Patil D.; Preethi N.
source: Lecture Notes on Data Engineering and Communications Technologies, Vol-131, pp. 341-349.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23674512
format: Online
language: English
type: Book chapter
coverage: Patil D., Christ University, Bengaluru, India; Preethi N., Christ University, Bengaluru, India