RATE: Recommendation-aware Trust Evaluation in Online Social Networks

Abstract

In online social networks (OSNs), it is an open challenge to select proper recommenders for predicting the trustworthiness of a target. In real life, people who are close and influential to us can usually make more proper and acceptable recommendations. Based on this observation, we present the idea of recommendation-aware trust evaluation (RATE). We further model the recommender selection problem into an optimal problem, with the objectives of higher accuracy, lower risk (uncertainty), and less cost. Four metrics, trustworthiness, influence, uncertainty, and cost, are identified to measure the quality of recommenders. Experimental results, with the real social network data set of Epinions, validate the effectiveness of RATE: it can predict trust with higher accuracy (at least 24.64% higher), lower risk, and less cost (about 30% lower).