A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity

This paper presented a new similarity method to improve the accuracy of traditional Collaborative Filtering (CF) method under sparse data issue. CF provides the user with items, that what they need, based on analyses the preferences of users who have a strong correlation to him/her preference. Howev...

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Main Authors:	Al-Bashiri, Hael, Abdulgabber, Mansoor Abdullateef, Awanis, Romli, Norazuwa, Salehudin
Format:	Article
Language:	English
Published:	The Science and Information (SAI) Organization Limited 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/21368/ http://umpir.ump.edu.my/id/eprint/21368/ http://umpir.ump.edu.my/id/eprint/21368/ http://umpir.ump.edu.my/id/eprint/21368/1/A%20Developed%20Collaborative%20Filtering%20Similarity-fskkp-2018.pdf

id	ump-21368
recordtype	eprints
spelling	ump-213682019-10-18T02:31:44Z http://umpir.ump.edu.my/id/eprint/21368/ A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity Al-Bashiri, Hael Abdulgabber, Mansoor Abdullateef Awanis, Romli Norazuwa, Salehudin QA75 Electronic computers. Computer science This paper presented a new similarity method to improve the accuracy of traditional Collaborative Filtering (CF) method under sparse data issue. CF provides the user with items, that what they need, based on analyses the preferences of users who have a strong correlation to him/her preference. However, the accuracy is influencing by the method that use to find neighbors. Pearson correlation coefficient and Cosine measures, as the most widely used methods, depending on the rating of only co-rated items to find the correlations between users. Consequently, these methods have lack of ability in addressing the sparsity. This paper presented a new proposed similarity method based on the global user preference to address the sparsity issue and improve the accuracy of recommendation. Thus, the novelty of this method is the ability to solve the similarity issue with a capability of finding the relationship among non-correlated users. Furthermore, to determine the right neighbors during the process of computing the similarity between a pair of users, the developed method considered two main factors (fairness and proportion of co-rated). The MovieLens 100K benchmark dataset is used to evaluate the developed method accuracy. The experiments’ result showed that the accuracy of the developed method is improved compared to the traditional CF similarity methods using a specific common CF evaluation metrics. The Science and Information (SAI) Organization Limited 2018 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/21368/1/A%20Developed%20Collaborative%20Filtering%20Similarity-fskkp-2018.pdf Al-Bashiri, Hael and Abdulgabber, Mansoor Abdullateef and Awanis, Romli and Norazuwa, Salehudin (2018) A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity. International Journal of Advanced Computer Science and Applications (IJACSA), 9 (4). pp. 135-142. ISSN 2156-5570(Online) http://dx.doi.org/10.14569/IJACSA.2018.090423 doi: 10.14569/IJACSA.2018.090423
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Al-Bashiri, Hael Abdulgabber, Mansoor Abdullateef Awanis, Romli Norazuwa, Salehudin A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity
description	This paper presented a new similarity method to improve the accuracy of traditional Collaborative Filtering (CF) method under sparse data issue. CF provides the user with items, that what they need, based on analyses the preferences of users who have a strong correlation to him/her preference. However, the accuracy is influencing by the method that use to find neighbors. Pearson correlation coefficient and Cosine measures, as the most widely used methods, depending on the rating of only co-rated items to find the correlations between users. Consequently, these methods have lack of ability in addressing the sparsity. This paper presented a new proposed similarity method based on the global user preference to address the sparsity issue and improve the accuracy of recommendation. Thus, the novelty of this method is the ability to solve the similarity issue with a capability of finding the relationship among non-correlated users. Furthermore, to determine the right neighbors during the process of computing the similarity between a pair of users, the developed method considered two main factors (fairness and proportion of co-rated). The MovieLens 100K benchmark dataset is used to evaluate the developed method accuracy. The experiments’ result showed that the accuracy of the developed method is improved compared to the traditional CF similarity methods using a specific common CF evaluation metrics.
format	Article
author	Al-Bashiri, Hael Abdulgabber, Mansoor Abdullateef Awanis, Romli Norazuwa, Salehudin
author_facet	Al-Bashiri, Hael Abdulgabber, Mansoor Abdullateef Awanis, Romli Norazuwa, Salehudin
author_sort	Al-Bashiri, Hael
title	A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity
title_short	A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity
title_full	A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity
title_fullStr	A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity
title_full_unstemmed	A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity
title_sort	developed collaborative filtering similarity method to improve the accuracy of recommendations under data sparsity
publisher	The Science and Information (SAI) Organization Limited
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/21368/ http://umpir.ump.edu.my/id/eprint/21368/ http://umpir.ump.edu.my/id/eprint/21368/ http://umpir.ump.edu.my/id/eprint/21368/1/A%20Developed%20Collaborative%20Filtering%20Similarity-fskkp-2018.pdf
first_indexed	2023-09-18T22:31:19Z
last_indexed	2023-09-18T22:31:19Z
_version_	1777416304560439296

A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity

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