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

Wei Peng,Baogui Xin

doi:10.1186/s13638-019-1600-4

Abstract

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.

Highlights

The Internet has brought about industrial change and nurtured e-commerce
3 Methodology Traditional social recommendation algorithms ignored 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
7 Conclusions 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

Summary

Introduction

E-commerce has generated huge amount of network information, which results in information overload [1]. Information overload directly increases the difficulty of selecting products and inspires people to seek effective solutions. The first type of solution can save information retrieval time, but it is easy to miss lots of useful information. The second type of solution is to classify the project according to the similarity feature chosen by the user; this can overcome the defects of the first type of solution, but it has the disadvantage of low efficiency and poor precision. The third type of solution allows users to retrieve and filter irrelevant information by keywords, which can solve the problem of the second type of solution but cannot consider the user’s individualized

Methods

Results

Conclusion