Pair-wise trust prediction employing matrix factorization for online social network

Abstract

Online social networks become popular as a medium for propagating information and connecting like-minded people. The public accessibility of such networks with the ability to share opinions, thoughts, information, and experience offers great potential to enterprises and governments. In addition to individuals using such networks connect them to their friends and families, governments and enterprises and started exploiting these platforms for delivering their services to citizens and customers. However, the success of such attempts relies on the trust level with each other also with the service provider. Therefore, trust becomes an essential and important element of a successful social network. Matrix factorization is one of the state-of-the-art recommender systems. SDV and SDV+ are used for trust-based recommender system. SDV++ is used for both internal and external factors that affect trust. This paper proposed a novel method to predict trust by Novel SDV++ Matrix factorization techniques that use both propagation and latent factor approach to predict more accurate results.