Dining establishment recommendation system using machine learning / Muhammad Hazwan Mohd Hazhar

Recommendation systems are now widely implemented across various domains in the modern technological landscape, including e-commerce platforms like Shopee, Amazon, and Lazada, as well as movie streaming services such as Netflix, Hulu, and Disney Plus. Among the many methods used in recommendation sy...

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Main Author:	Mohd Hazhar, Muhammad Hazwan
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Finite element method
Online Access:	https://ir.uitm.edu.my/id/eprint/105933/1/105933.pdf
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id	my-uitm-ir.105933
record_format	uketd_dc
spelling	my-uitm-ir.1059332024-11-30T23:08:40Z Dining establishment recommendation system using machine learning / Muhammad Hazwan Mohd Hazhar 2024 Mohd Hazhar, Muhammad Hazwan Finite element method Recommendation systems are now widely implemented across various domains in the modern technological landscape, including e-commerce platforms like Shopee, Amazon, and Lazada, as well as movie streaming services such as Netflix, Hulu, and Disney Plus. Among the many methods used in recommendation systems, Matrix Factorization (MF) stands out as a key technique within collaborative filtering (CF). The project intends to develop a dining establishment recommendation system for Malaysian customers using Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) methods that are the specific type of MF. The study investigates the usefulness of various MF approaches by analysing a secondary dataset of user ratings and reviews for more than 800 restaurants. The system seeks to improve user happiness by making personalised suggestions based on their interests and location. The results show that PCA surpasses SVD in terms of Root Mean Square Error (RMSE), making it the preferable approach for creating accurate and efficient meal suggestions. The project features a user-friendly interface created using Streamlit that allows users to pick their location and obtain top eating recommendations, which are then enhanced by analysing relevant user evaluations with TF-IDF and cosine similarity. 2024 Thesis https://ir.uitm.edu.my/id/eprint/105933/ https://ir.uitm.edu.my/id/eprint/105933/1/105933.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Norddin, Nur Idalisa
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Norddin, Nur Idalisa
topic	Finite element method
spellingShingle	Finite element method Mohd Hazhar, Muhammad Hazwan Dining establishment recommendation system using machine learning / Muhammad Hazwan Mohd Hazhar
description	Recommendation systems are now widely implemented across various domains in the modern technological landscape, including e-commerce platforms like Shopee, Amazon, and Lazada, as well as movie streaming services such as Netflix, Hulu, and Disney Plus. Among the many methods used in recommendation systems, Matrix Factorization (MF) stands out as a key technique within collaborative filtering (CF). The project intends to develop a dining establishment recommendation system for Malaysian customers using Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) methods that are the specific type of MF. The study investigates the usefulness of various MF approaches by analysing a secondary dataset of user ratings and reviews for more than 800 restaurants. The system seeks to improve user happiness by making personalised suggestions based on their interests and location. The results show that PCA surpasses SVD in terms of Root Mean Square Error (RMSE), making it the preferable approach for creating accurate and efficient meal suggestions. The project features a user-friendly interface created using Streamlit that allows users to pick their location and obtain top eating recommendations, which are then enhanced by analysing relevant user evaluations with TF-IDF and cosine similarity.
format	Thesis
qualification_level	Bachelor degree
author	Mohd Hazhar, Muhammad Hazwan
author_facet	Mohd Hazhar, Muhammad Hazwan
author_sort	Mohd Hazhar, Muhammad Hazwan
title	Dining establishment recommendation system using machine learning / Muhammad Hazwan Mohd Hazhar
title_short	Dining establishment recommendation system using machine learning / Muhammad Hazwan Mohd Hazhar
title_full	Dining establishment recommendation system using machine learning / Muhammad Hazwan Mohd Hazhar
title_fullStr	Dining establishment recommendation system using machine learning / Muhammad Hazwan Mohd Hazhar
title_full_unstemmed	Dining establishment recommendation system using machine learning / Muhammad Hazwan Mohd Hazhar
title_sort	dining establishment recommendation system using machine learning / muhammad hazwan mohd hazhar
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/105933/1/105933.pdf
_version_	1818588162638741504

Dining establishment recommendation system using machine learning / Muhammad Hazwan Mohd Hazhar

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