Inferring and calculating trust for trust-based recommendations

The exponential growth of social media services led to the information overload problem which information filtering and recommender systems deal by exploiting various techniques. One popular technique for making recommendations is based on trust statements between users in a social network. However, current approaches face limitations. As a solution to overcome many of these limitations current paper studies a novel method to infer trust relationships. The method is based on the triadic closure mechanism, which is a fundamental mechanism of link formation in social networks via which communities emerge naturally, especially when the network is very sparse. Additionally, a method called JaccardCoefficient is proposed to calculate the trust weight of the inferred relationships based on the Jaccard Coefficient similarity measure. Both methods are evaluated with real-world datasets and are compared with other state-of-the-art methods.