A Comparison of Similarity Measures in an Online Book Recommendation System

Title

A Comparison of Similarity Measures in an Online Book Recommendation System

Creator

Patil D.; Preethi N.

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.

Source

Lecture Notes on Data Engineering and Communications Technologies, Vol-131, pp. 341-349.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Books; Collaborative filtering; Machine learning; Recommendation system; Similarity; User

Coverage

Patil D., Christ University, Bengaluru, India; Preethi N., Christ University, Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 23674512

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-981-19-1844-5_26

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134737304&doi=10.1007%2f978-981-19-1844-5_26&partnerID=40&md5=4f6a88b672849280d8651b8ee3b06720