The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2019.0101218
PDF

Memory-based Collaborative Filtering: Impacting of Common Items on the Quality of Recommendation

Author 1: Hael Al-bashiri
Author 2: Hasan Kahtan
Author 3: Mansoor Abdullateef Abdulgabber
Author 4: Awanis Romli
Author 5: Mohammad Adam Ibrahim Fakhreldin

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 12, 2019.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: In this study, the impact of the common items between a pair of users on the accuracy of memory-based collaborative filtering (CF) is investigated. Although CF systems are a widely used recommender system, data sparsity remains an issue. As a result, the similarity weight between a pair of users with few ratings is almost a fake relationship. In this work, the similarity weight of the traditional similarity methods is determined using exponential functions with various thresholds. These thresholds are used to specify the size of the common items amongst the users. Exponential functions can devalue the similarity weight between a pair of users who has few common items and increase the similarity weight for users who have sufficient co-rated items. Therefore, the pair of users with sufficient co-rated items obtains a stronger relationship than those with few common items. Thus, the significance of this paper is to succinctly test the impacting of common items on the quality of recommendation that creates an understanding for the researchers by discussing the findings presented in this study. The MovieLens datasets are used as benchmark datasets to measure the effect of the ratio of common items on the accuracy. The result verifies the considerable impact exerted by the factor of common items.

Keywords: Collaborative filtering; memory-based; similarity method; data sparsity

Hael Al-bashiri, Hasan Kahtan, Mansoor Abdullateef Abdulgabber, Awanis Romli and Mohammad Adam Ibrahim Fakhreldin, “Memory-based Collaborative Filtering: Impacting of Common Items on the Quality of Recommendation” International Journal of Advanced Computer Science and Applications(IJACSA), 10(12), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101218

@article{Al-bashiri2019,
title = {Memory-based Collaborative Filtering: Impacting of Common Items on the Quality of Recommendation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101218},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101218},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {12},
author = {Hael Al-bashiri and Hasan Kahtan and Mansoor Abdullateef Abdulgabber and Awanis Romli and Mohammad Adam Ibrahim Fakhreldin}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org