Trustor clustering with an improved recommender system based on social relationships

Abstract

As we face a deluge of information in the modern world, the importance of recommender systems (RSs) that recommend relevant items to users has increased. The majority of existing RS schemes observe the prior ratings history of consumers to identify preferred items. However, current RSs suffer from the cold start problem, and their performance is dismal when new users or items appear. In order to address the cold start problem, a new type of solution that exploits social network features has been proposed. Many such social RSs analyze trustor–trustee relationships to discover latent social features shared between trustor and trustee. Since social relationships between trustors and trustees are directed, but not reciprocal, it is not guaranteed that a trustee has features in common with its trustors. Moreover, existing schemes are based on the assumption of independence between trustors who follow the same trustee, and therefore fail to recognize quintessential factors shared by the trustors.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.