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.2020.0111262
PDF

Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

Author 1: Ismail Ahmed Al-Qasem Al-Hadi
Author 2: Mohammad Ahmed Alomari
Author 3: Eissa M. Alshari
Author 4: Waheed Ali H. M. Ghanem
Author 5: Safwan M Ghaleb

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 12, 2020.

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

Abstract: Collaborative Filtering (CF) is a widely used technique in recommendation systems. It provides personal recommendations for users based on their preferences. However, this technique suffers from the sparsity issue which occurs due to a high proportion of missing rating scores in a rating matrix. Several factorization approaches have been used to address the sparsity issue. Such techniques have also been considered to tackle other challenges such as the overfitted predicted scores. Nevertheless, they suffer from setbacks such as drift in user preferences and items’ popularity decay. These challenges can be solved by prediction approaches that accurately learn the long-term and short-term preferences integrated with factorization features. Nonetheless, the current temporal-based factorization approaches do not accurately learn the convergence of the assigned k clusters due to a lower number of short-term periods. Additionally, the use of optimization algorithms in the learning process to reduce prediction errors is time-consuming which necessitates a faster optimization algorithm. To address these issues, a new temporal-based approach named TWOCF is proposed in this paper. TWOCF utilizes the elbow clustering method to define the optimal number of clusters for the temporal activities of both users and items. This approach deploys the whale optimization algorithm to accurately learn short-term preferences within other factorization and temporal features. Experimental results indicate that TWOCF exhibits a superior CF prediction accuracy achieved within a shorter execution time when compared to the benchmark approaches.

Keywords: Collaborative filtering; matrix factorization; temporal-based approaches; whale optimization

Ismail Ahmed Al-Qasem Al-Hadi, Mohammad Ahmed Alomari, Eissa M. Alshari, Waheed Ali H. M. Ghanem and Safwan M Ghaleb, “Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering” International Journal of Advanced Computer Science and Applications(IJACSA), 11(12), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111262

@article{Al-Hadi2020,
title = {Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111262},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111262},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {12},
author = {Ismail Ahmed Al-Qasem Al-Hadi and Mohammad Ahmed Alomari and Eissa M. Alshari and Waheed Ali H. M. Ghanem and Safwan M Ghaleb}
}



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