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primary topic: Book Recommendation System (BRS) Using Collaboration Filtering Machine Learning

Book Recommendation System (BRS) Using Collaboration Filtering Machine Learning
type: AcademicArticle
type: Article
abstract: The project is about the exploration of the world of book recommendation system with the primary objective of delivering personalized book recommendations to an extensive audience of book passions and readers. Firstly, the project will give the significance of recommendation system and their widespread application. In where it outlines the research objectives which emphasizing the development and deployment of an effective BRS. Hence, it will be discussed into the data preprocessing phases where the project prepares and cleaned the four dataset that will be provided. Also, various of data preprocessing techniques are used including handling missing values, outliers’ detection and data standardization. Then, it will be discussed on the creation and evaluation of three recommendation model which are K-Nearest Neighbors (KNN), Support Vector Classification (SVC) and Neural Collaborative Filtering (NCF). The model is assessed based on performance metrics such as Mean Squared Error (MSE) and Root Mean Square Error (RMSE). The comparative analysis informs the selection of the most effective model for deployment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
date: 2024
volume: 545
is part of: Studies in Systems, Decision and Control
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

Springer Science and Business Media Deutschland GmbH
type: Organization

Studies in Systems, Decision and Control
type: Collection
issn: 21984182

Anis Nabilah
type: Person
given name: Anis
family name: Nabilah

Halimaton Hakimi
type: Person
given name: Halimaton
family name: Hakimi

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primary topic: Book Recommendation System (BRS) Using Collaboration Filtering Machine Learning