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.
@article{arxiv.1903.04489,
title = {SPMF: A Social Trust and Preference Segmentation-based Matrix Factorization Recommendation Algorithm},
author = {Wei Peng and Baogui Xin},
journal= {arXiv preprint arXiv:1903.04489},
year = {2019}
}