Enhancing Matrix Factorization-based Recommender Systems via Graph Neural Networks

Zhiwei Guo, Dian Meng, Huiyan Zhang, Heng Wang, Keping Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Due to the serious information overload problem caused by the rapid development of the Internet, recommender system (RS) has been one of the most concerned technologies in the past decade. Accompanied with the prevalence of social networks, social information is usually introduced into RS to pursue higher recommendation efficiency, yielding the research of social recommendations (SoR). Almost all of existing researches of SoR just consider the influence of social relationships, yet ignoring the fact that correlations exist among item attributes and will certainly influence social choices. Therefore, this work introduces the graph neural networks to enhance matrix factorization-based recommender systems. and the proposal in this work is named GNN-MF for short. The user subspace and item subspace in matrix factorization are represented with the use of deep neural networks, in which parameters are learned by back propagation. The experiments well prove efficiency of the GNN-MF.

Original languageEnglish
Title of host publication19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1053-1059
Number of pages7
ISBN (Electronic)9781665435741
DOIs
Publication statusPublished - 2021
Event19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 - New York, United States
Duration: 2021 Sep 302021 Oct 3

Publication series

Name19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

Conference

Conference19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
Country/TerritoryUnited States
CityNew York
Period21/9/3021/10/3

Keywords

  • Deep learning
  • Graph neural networks
  • Matrix factorization
  • Recommender systems

ASJC Scopus subject areas

  • Communication
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Renewable Energy, Sustainability and the Environment

Fingerprint

Dive into the research topics of 'Enhancing Matrix Factorization-based Recommender Systems via Graph Neural Networks'. Together they form a unique fingerprint.

Cite this