Book recommendation system using content-based filtering / Amalin Aliesya Mohd Azmi

In the modern world, where individuals may enter a library or browse online platforms without a specific book in mind. However, every reader has their own unique interests and preferences. With the help of Book Recommendation System, we can offer a solution by utilizing algorithms to suggest books b...

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Main Author:	Mohd Azmi, Amalin Aliesya
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Programming Rule-based programming Backtrack programming
Online Access:	https://ir.uitm.edu.my/id/eprint/96308/1/96308.pdf
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id	my-uitm-ir.96308
record_format	uketd_dc
spelling	my-uitm-ir.963082024-06-04T07:20:32Z Book recommendation system using content-based filtering / Amalin Aliesya Mohd Azmi 2023 Mohd Azmi, Amalin Aliesya Programming. Rule-based programming. Backtrack programming In the modern world, where individuals may enter a library or browse online platforms without a specific book in mind. However, every reader has their own unique interests and preferences. With the help of Book Recommendation System, we can offer a solution by utilizing algorithms to suggest books based on a reader’s interests. With the aim of reducing the need to search for books and providing personalized recommendations, we propose the development of a website for students, designed to simplify the book selection process and eliminate confusion. By leveraging a user’s previous checkout history and search data, the website can recommend books based on the user’s interests. There are several models available for building such a system, using Content-based recommendation. However, most machine learning-based techniques rely on manually produced features derived from the input textual content. The models' performance was evaluated using a confusion matrix, where the accuracy, TF-IDF, cosine similarity, mean average precision were calculated. The highest accuracy obtained for the model is 82%. Overall, all objectives in this project are achieved successfully. However, there are also so limitations that needed improvement in the future. The major limitation is the algorithm running speed which can be a little slow when it comes to making recommendations. This is attributed to the intricacy of the content-based filtering algorithm, necessitating a substantial computational load on the central processing unit of a computer. In the future, the project can include the ability to explore multi-modal recommendation systems that consider not only textual information but also cover images, author details, and other relevant features to provide a more holistic understanding of user preferences. 2023 Thesis https://ir.uitm.edu.my/id/eprint/96308/ https://ir.uitm.edu.my/id/eprint/96308/1/96308.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Anuar, Khairul
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Anuar, Khairul
topic	Programming Rule-based programming Backtrack programming
spellingShingle	Programming Rule-based programming Backtrack programming Mohd Azmi, Amalin Aliesya Book recommendation system using content-based filtering / Amalin Aliesya Mohd Azmi
description	In the modern world, where individuals may enter a library or browse online platforms without a specific book in mind. However, every reader has their own unique interests and preferences. With the help of Book Recommendation System, we can offer a solution by utilizing algorithms to suggest books based on a reader’s interests. With the aim of reducing the need to search for books and providing personalized recommendations, we propose the development of a website for students, designed to simplify the book selection process and eliminate confusion. By leveraging a user’s previous checkout history and search data, the website can recommend books based on the user’s interests. There are several models available for building such a system, using Content-based recommendation. However, most machine learning-based techniques rely on manually produced features derived from the input textual content. The models' performance was evaluated using a confusion matrix, where the accuracy, TF-IDF, cosine similarity, mean average precision were calculated. The highest accuracy obtained for the model is 82%. Overall, all objectives in this project are achieved successfully. However, there are also so limitations that needed improvement in the future. The major limitation is the algorithm running speed which can be a little slow when it comes to making recommendations. This is attributed to the intricacy of the content-based filtering algorithm, necessitating a substantial computational load on the central processing unit of a computer. In the future, the project can include the ability to explore multi-modal recommendation systems that consider not only textual information but also cover images, author details, and other relevant features to provide a more holistic understanding of user preferences.
format	Thesis
qualification_level	Bachelor degree
author	Mohd Azmi, Amalin Aliesya
author_facet	Mohd Azmi, Amalin Aliesya
author_sort	Mohd Azmi, Amalin Aliesya
title	Book recommendation system using content-based filtering / Amalin Aliesya Mohd Azmi
title_short	Book recommendation system using content-based filtering / Amalin Aliesya Mohd Azmi
title_full	Book recommendation system using content-based filtering / Amalin Aliesya Mohd Azmi
title_fullStr	Book recommendation system using content-based filtering / Amalin Aliesya Mohd Azmi
title_full_unstemmed	Book recommendation system using content-based filtering / Amalin Aliesya Mohd Azmi
title_sort	book recommendation system using content-based filtering / amalin aliesya mohd azmi
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/96308/1/96308.pdf
_version_	1804889984506789888

Book recommendation system using content-based filtering / Amalin Aliesya Mohd Azmi

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