A social trust and preference segmentation-based matrix factorization recommendation algorithm

• Published in: EURASIP journal on wireless communications and networking Vol. 2019; no. 1; pp. 1 - 12
• Main Authors: Peng, Wei, Xin, Baogui
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 16.12.2019; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Collaborative filtering; Communications Engineering; Data sparsity; Edge; Engineering; Fog; Human-centered Computing in Cloud; Information Systems Applications (incl.Internet); Machine learning; Networks; Personalized recommendation; Preference similarity; Preferences; Segmentation; Signal,Image and Speech Processing; Trust relationship; Personalized recommendation; Trust relationship; Preference similarity; Collaborative filtering; Data sparsity; Machine learning
• ISSN: 1687-1499; 1687-1472; 1687-1499
• DOI: 10.1186/s13638-019-1600-4

A recommendation can inspire potential demands of users and make e-commerce platforms more intelligent and is essential for e-commerce enterprises’ sustainable development. The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. To solve these problems mentioned above, we propose a social trust and preference segmentation-based matrix factorization (SPMF) recommendation algorithm. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly superior to that of some state-of-the-art recommendation algorithms. The SPMF algorithm is a better recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.