Article recommendation system using content-based filtering / Muhammad Hazrul Afiq Kamaruddin

The challenges in the education industry include the time-consuming process of manually searching for relevant research articles, which reduces productivity and negatively impacts academic performance. Furthermore, the growing issue of information overload and anxiety among students and researchers...

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Main Author:	Kamaruddin, Muhammad Hazrul Afiq
Format:	Thesis
Language:	English
Published:	2024
Subjects:	System design
Online Access:	https://ir.uitm.edu.my/id/eprint/95722/1/95722.pdf
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id	my-uitm-ir.95722
record_format	uketd_dc
spelling	my-uitm-ir.957222024-05-23T01:48:35Z Article recommendation system using content-based filtering / Muhammad Hazrul Afiq Kamaruddin 2024 Kamaruddin, Muhammad Hazrul Afiq System design The challenges in the education industry include the time-consuming process of manually searching for relevant research articles, which reduces productivity and negatively impacts academic performance. Furthermore, the growing issue of information overload and anxiety among students and researchers raises the risk of burnout and decreases overall academic performance. In this study, article recommendation system using content-based filtering was designed and developed to address the challenges. The algorithm used is able to generate relevant article recommendations based on the content of the article. The algorithm consists of two components which are Term Frequency - Inverse Document Frequency (TF-IDF) and Cosine Similarity. TF-IDF calculates the weightage of user query and each keyword in each article. Vectors containing weightage values for both user query and articles in dataset will be calculated using Cosine Similarity to obtain similarity value. Articles recommendation will be generated after sorting and filtering based on threshold value. The result was evaluated using confusion matrix and evaluation metrics such accuracy, precision, recall and F1 score. The article recommendation system is able to achieve up to 99% accuracy, 86% precision, 76% recall and F1 score of 0.8 where the threshold value is 0.1. Overall, the project is successful as it is able to generate relevant articles accurately. 2024 Thesis https://ir.uitm.edu.my/id/eprint/95722/ https://ir.uitm.edu.my/id/eprint/95722/1/95722.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Anuar, Nurhilyana
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Anuar, Nurhilyana
topic	System design
spellingShingle	System design Kamaruddin, Muhammad Hazrul Afiq Article recommendation system using content-based filtering / Muhammad Hazrul Afiq Kamaruddin
description	The challenges in the education industry include the time-consuming process of manually searching for relevant research articles, which reduces productivity and negatively impacts academic performance. Furthermore, the growing issue of information overload and anxiety among students and researchers raises the risk of burnout and decreases overall academic performance. In this study, article recommendation system using content-based filtering was designed and developed to address the challenges. The algorithm used is able to generate relevant article recommendations based on the content of the article. The algorithm consists of two components which are Term Frequency - Inverse Document Frequency (TF-IDF) and Cosine Similarity. TF-IDF calculates the weightage of user query and each keyword in each article. Vectors containing weightage values for both user query and articles in dataset will be calculated using Cosine Similarity to obtain similarity value. Articles recommendation will be generated after sorting and filtering based on threshold value. The result was evaluated using confusion matrix and evaluation metrics such accuracy, precision, recall and F1 score. The article recommendation system is able to achieve up to 99% accuracy, 86% precision, 76% recall and F1 score of 0.8 where the threshold value is 0.1. Overall, the project is successful as it is able to generate relevant articles accurately.
format	Thesis
qualification_level	Bachelor degree
author	Kamaruddin, Muhammad Hazrul Afiq
author_facet	Kamaruddin, Muhammad Hazrul Afiq
author_sort	Kamaruddin, Muhammad Hazrul Afiq
title	Article recommendation system using content-based filtering / Muhammad Hazrul Afiq Kamaruddin
title_short	Article recommendation system using content-based filtering / Muhammad Hazrul Afiq Kamaruddin
title_full	Article recommendation system using content-based filtering / Muhammad Hazrul Afiq Kamaruddin
title_fullStr	Article recommendation system using content-based filtering / Muhammad Hazrul Afiq Kamaruddin
title_full_unstemmed	Article recommendation system using content-based filtering / Muhammad Hazrul Afiq Kamaruddin
title_sort	article recommendation system using content-based filtering / muhammad hazrul afiq kamaruddin
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/95722/1/95722.pdf
_version_	1804889972483817472

Article recommendation system using content-based filtering / Muhammad Hazrul Afiq Kamaruddin

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