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.2023.0140994
PDF

Group Intelligence Recommendation System based on Knowledge Graph and Fusion Recommendation Model

Author 1: Chengning Huang
Author 2: Bo Jing
Author 3: Lili Jiang
Author 4: Yuquan Zhu

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 9, 2023.

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

Abstract: The challenge of how to further improve the accuracy of the system's recommendations in a data-limited environment is crucial as the use of group intelligence recommendation systems in everyday life increases. Through the fusion of different types of auxiliary information, this study develops a multi-feature fusion model based on the conventional recommendation model by introducing knowledge graphs. It also considers the homogeneity of push results caused by graph convolutional network smoothing when using knowledge graphs, and designs a fusion label propagation algorithm and graph convolution. The multi-feature fusion model had a maximum hit rate of over 80% and a normalised discount gain of up to 43% running time much lower than the conventional graph convolution recommendation model in the representation dimension interval [2, 32], while the fusion label propagation algorithm and graph convolution network model maintained a hit rate and normalised discount gain higher than the conventional model by 2 to 1 under 10 consecutive epochs. With a hit rate and normalised discount gain 2 to 10 percentage points higher than the conventional model, the coverage rate increased to 49.8%. This study is useful for research on group intelligence recommendation systems and can serve as a technical guide for improving the ability of group intelligence systems to make recommendations quickly.

Keywords: Knowledge graphs; recommendation system; graph convolutional networks; label propagation algorithms

Chengning Huang, Bo Jing, Lili Jiang and Yuquan Zhu, “Group Intelligence Recommendation System based on Knowledge Graph and Fusion Recommendation Model” International Journal of Advanced Computer Science and Applications(IJACSA), 14(9), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140994

@article{Huang2023,
title = {Group Intelligence Recommendation System based on Knowledge Graph and Fusion Recommendation Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140994},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140994},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {9},
author = {Chengning Huang and Bo Jing and Lili Jiang and Yuquan Zhu}
}



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