TY - GEN
T1 - Enhancing Matrix Factorization-based Recommender Systems via Graph Neural Networks
AU - Guo, Zhiwei
AU - Meng, Dian
AU - Zhang, Huiyan
AU - Wang, Heng
AU - Yu, Keping
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Deep learning
KW - Graph neural networks
KW - Matrix factorization
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=85124142830&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124142830&partnerID=8YFLogxK
U2 - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00146
DO - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00146
M3 - Conference contribution
AN - SCOPUS:85124142830
T3 - 19th 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
SP - 1053
EP - 1059
BT - 19th 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
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th 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
Y2 - 30 September 2021 through 3 October 2021
ER -
