The improvement of Item-based collaborative filtering algorithm in recommendation system using similarity index

The extensive and increase use of high-tech product in business has generated a huge amount of business information to be processed in many fields. Thus, a recommendation system is introduced as an effective strategy to manage the business information overload problem. The system aims to filters eno...

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Main Author:	Yao, Ma
Format:	Thesis
Language:	eng eng
Published:	2022
Subjects:	T58.6-58.62 Management information systems HF5001-6182 Business T Technology (General)
Online Access:	https://etd.uum.edu.my/9764/1/permission%20to%20deposit-grant%20the%20permission-823872.pdf https://etd.uum.edu.my/9764/2/s823872_01.pdf
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spelling	my-uum-etd.97642022-08-17T08:03:42Z The improvement of Item-based collaborative filtering algorithm in recommendation system using similarity index 2022 Yao, Ma Saip, Mohamed Ali Ab Aziz, Azizi Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Art & Sciences T58.6-58.62 Management information systems HF5001-6182 Business T Technology (General) The extensive and increase use of high-tech product in business has generated a huge amount of business information to be processed in many fields. Thus, a recommendation system is introduced as an effective strategy to manage the business information overload problem. The system aims to filters enormous information and proposes appropriate suggestions to users. A collaborative filtering algorithm is one of the algorithms applied in the recommendation system. However, the collaborative filtering algorithm faces cold-start problem, where new items in the shopping list are not identified and recognized by the system. Hence, this study proposes an improved collaborative filtering algorithm which aims to alleviate the cold-start problem by combining the item rating and item attributes in similarity index. The performance of enhanced algorithm was compared to existing collaborative filtering algorithms in term of precision rate, recall rate and F1 score using Movielens dataset. The algorithm’s efficiency, objectiveness, and accurateness towards its performances were measured. Finally, the experimental results showed that the proposed algorithm get 15 percent precision rate, 6 percent recall rate and 9 percent F1 score. Thus, it proved to be more effective in deal with cold-start problems by using new similarity index, and also can make recommendations on new items in different fields with satisfactory accuracy for better recommendation result. Theoretically, this study contributes to improve the collaborative filtering algorithm in recommendation system for overcome the cold-start problem by analyzing more item attributes to extract more information to the algorithm. Besides, the proposed algorithms can be applied in many fields for cold-items recommendation and to enhance the quality of the recommendation system. 2022 Thesis https://etd.uum.edu.my/9764/ https://etd.uum.edu.my/9764/1/permission%20to%20deposit-grant%20the%20permission-823872.pdf text eng staffonly https://etd.uum.edu.my/9764/2/s823872_01.pdf text eng public other masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Saip, Mohamed Ali Ab Aziz, Azizi
topic	T58.6-58.62 Management information systems HF5001-6182 Business T Technology (General)
spellingShingle	T58.6-58.62 Management information systems HF5001-6182 Business T Technology (General) Yao, Ma The improvement of Item-based collaborative filtering algorithm in recommendation system using similarity index
description	The extensive and increase use of high-tech product in business has generated a huge amount of business information to be processed in many fields. Thus, a recommendation system is introduced as an effective strategy to manage the business information overload problem. The system aims to filters enormous information and proposes appropriate suggestions to users. A collaborative filtering algorithm is one of the algorithms applied in the recommendation system. However, the collaborative filtering algorithm faces cold-start problem, where new items in the shopping list are not identified and recognized by the system. Hence, this study proposes an improved collaborative filtering algorithm which aims to alleviate the cold-start problem by combining the item rating and item attributes in similarity index. The performance of enhanced algorithm was compared to existing collaborative filtering algorithms in term of precision rate, recall rate and F1 score using Movielens dataset. The algorithm’s efficiency, objectiveness, and accurateness towards its performances were measured. Finally, the experimental results showed that the proposed algorithm get 15 percent precision rate, 6 percent recall rate and 9 percent F1 score. Thus, it proved to be more effective in deal with cold-start problems by using new similarity index, and also can make recommendations on new items in different fields with satisfactory accuracy for better recommendation result. Theoretically, this study contributes to improve the collaborative filtering algorithm in recommendation system for overcome the cold-start problem by analyzing more item attributes to extract more information to the algorithm. Besides, the proposed algorithms can be applied in many fields for cold-items recommendation and to enhance the quality of the recommendation system.
format	Thesis
qualification_name	other
qualification_level	Master's degree
author	Yao, Ma
author_facet	Yao, Ma
author_sort	Yao, Ma
title	The improvement of Item-based collaborative filtering algorithm in recommendation system using similarity index
title_short	The improvement of Item-based collaborative filtering algorithm in recommendation system using similarity index
title_full	The improvement of Item-based collaborative filtering algorithm in recommendation system using similarity index
title_fullStr	The improvement of Item-based collaborative filtering algorithm in recommendation system using similarity index
title_full_unstemmed	The improvement of Item-based collaborative filtering algorithm in recommendation system using similarity index
title_sort	improvement of item-based collaborative filtering algorithm in recommendation system using similarity index
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2022
url	https://etd.uum.edu.my/9764/1/permission%20to%20deposit-grant%20the%20permission-823872.pdf https://etd.uum.edu.my/9764/2/s823872_01.pdf
_version_	1747828667760771072

The improvement of Item-based collaborative filtering algorithm in recommendation system using similarity index

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