Visible to the public A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree

TitleA Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree
Publication TypeConference Paper
Year of Publication2018
AuthorsLi, W., Zhu, H., Zhou, X., Shimizu, S., Xin, M., Jin, Q.
Conference Name2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)
Date Publishedaug
KeywordsCollaboration, collaborative filtering, data mining, Data Science, data sparsity, Dianping datasets, ecommerce, electronic commerce, Filtering, Human Behavior, human trust, Internet, Matrix decomposition, matrix factorization, novel personalized recommendation algorithm, personal characteristics, personalized recommendation, personalized recommendation technology, Prediction algorithms, pubcrawl, recommender systems, social network information, Social network services, social networking (online), Social trust, Training, Trust, trust relevancy degree, Trusted Computing
AbstractThe 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.
Citation Keyli_novel_2018