  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. A social trust and preference segmentation-based matrix
factorization (SPMF) recommendation system is proposed to solve the
above-mentioned problems. Experimental results based on the Ciao and Epinions
datasets show that the accuracy of the SPMF algorithm is significantly higher
than that of some state-of-the-art recommendation algorithms. The proposed SPMF
algorithm is a more accurate and effective recommendation algorithm based on
distinguishing the difference of trust relations and preference domain, which
can support commercial activities such as product marketing.
