Leveraging Community Detection for Accurate Trust Prediction

The aim of trust prediction is to infer trust values for pairs of users when the relationship between them is unknown. The unprecedented growth in the amount of online interactions on e-commerce websites has made the problem of predicting user trust relationships critically important, yet sparsity in the amount of known (labeled) relationships poses a significant challenge to the usage of machine learning techniques. This paper presents a community detection approach which leverages the network of available trust relations and rating similarities to compensate for the lack of labels. The key insight behind our framework is that trust values from the central community members can be used as a predictor for relationships between other community members. Here we evaluate the usage of two community detection algorithms, one of which works merely on the trust network while the other one uses both. Our algorithm outperforms other existing trust prediction methods on datasets from the wellknown product review websites Epinions and Ciao.