An improved memory-based collaborative filtering method based on the TOPSIS technique

This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined p...

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Main Authors:	Al-Bashiri, Hael, Abdulgabber, Mansoor Abdullateef, Awanis, Romli, Kahtan, Hasan
Format:	Article
Language:	English
Published:	Public Library of Science 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/22404/ http://umpir.ump.edu.my/id/eprint/22404/ http://umpir.ump.edu.my/id/eprint/22404/1/An%20improved%20memory-based%20collaborative%20filtering.pdf

id	ump-22404
recordtype	eprints
spelling	ump-224042019-10-18T02:32:16Z http://umpir.ump.edu.my/id/eprint/22404/ An improved memory-based collaborative filtering method based on the TOPSIS technique Al-Bashiri, Hael Abdulgabber, Mansoor Abdullateef Awanis, Romli Kahtan, Hasan QA75 Electronic computers. Computer science This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics. Public Library of Science 2018 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/22404/1/An%20improved%20memory-based%20collaborative%20filtering.pdf Al-Bashiri, Hael and Abdulgabber, Mansoor Abdullateef and Awanis, Romli and Kahtan, Hasan (2018) An improved memory-based collaborative filtering method based on the TOPSIS technique. PLoS ONE, 13 (10). pp. 1-26. ISSN 1932-6203 https://doi.org/10.1371/journal.pone.0204434
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 Kahtan, Hasan An improved memory-based collaborative filtering method based on the TOPSIS technique
description	This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics.
format	Article
author	Al-Bashiri, Hael Abdulgabber, Mansoor Abdullateef Awanis, Romli Kahtan, Hasan
author_facet	Al-Bashiri, Hael Abdulgabber, Mansoor Abdullateef Awanis, Romli Kahtan, Hasan
author_sort	Al-Bashiri, Hael
title	An improved memory-based collaborative filtering method based on the TOPSIS technique
title_short	An improved memory-based collaborative filtering method based on the TOPSIS technique
title_full	An improved memory-based collaborative filtering method based on the TOPSIS technique
title_fullStr	An improved memory-based collaborative filtering method based on the TOPSIS technique
title_full_unstemmed	An improved memory-based collaborative filtering method based on the TOPSIS technique
title_sort	improved memory-based collaborative filtering method based on the topsis technique
publisher	Public Library of Science
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22404/ http://umpir.ump.edu.my/id/eprint/22404/ http://umpir.ump.edu.my/id/eprint/22404/1/An%20improved%20memory-based%20collaborative%20filtering.pdf
first_indexed	2023-09-18T22:33:20Z
last_indexed	2023-09-18T22:33:20Z
_version_	1777416431251488768

An improved memory-based collaborative filtering method based on the TOPSIS technique

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