Trustor clustering with an improved recommender system based on social relationships

We posit that trustors who follow the same trustee have features in common. Based on the assumption that trustors who endorse the same trustee share similar tastes, we propose a new latent feature called Matrix S, and develop two novel RS algorithms that learn these latent features. We conduct an extensive performance evaluation using large scale real-world datasets, and observe that our proposed methods are not only more accurate than existing schemes but also show potential extensibility.