TY - GEN
T1 - A novel personalized recommendation algorithm based on trust relevancy degree
AU - Li, Weimin
AU - Zhu, Heng
AU - Zhou, Xiaokang
AU - Shimizu, Shohei
AU - Xin, Mingjun
AU - Jin, Qun
N1 - Funding Information:
ACKNOWLEDGE: This study is partially supported by National Natural Science Foundation of China (No.61762002).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - The rapid development of the Internet and ecommerce has brought a lot of convenience to people's life. Personalized recommendation technology provides users with services that they may be interested according to users' information such as personal characteristics and historical behaviors. The research of personalized recommendation has been a hot point of data mining and social networks. In this paper, we focus on resolving the problem of data sparsity based on users' rating data and social network information, introduce a set of new measures for social trust and propose a novel personalized recommendation algorithm based on matrix factorization combining trust relevancy. Our experiments were performed on the Dianping datasets. The results show that our algorithm outperforms traditional approaches in terms of accuracy and stability.
AB - The rapid development of the Internet and ecommerce has brought a lot of convenience to people's life. Personalized recommendation technology provides users with services that they may be interested according to users' information such as personal characteristics and historical behaviors. The research of personalized recommendation has been a hot point of data mining and social networks. In this paper, we focus on resolving the problem of data sparsity based on users' rating data and social network information, introduce a set of new measures for social trust and propose a novel personalized recommendation algorithm based on matrix factorization combining trust relevancy. Our experiments were performed on the Dianping datasets. The results show that our algorithm outperforms traditional approaches in terms of accuracy and stability.
KW - Collaborative filtering
KW - Matrix factorization
KW - Personalized recommendation
KW - Trust
UR - http://www.scopus.com/inward/record.url?scp=85056839084&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056839084&partnerID=8YFLogxK
U2 - 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00084
DO - 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00084
M3 - Conference contribution
AN - SCOPUS:85056839084
T3 - Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018
SP - 418
EP - 422
BT - Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018
Y2 - 12 August 2018 through 15 August 2018
ER -