Reputation features for trust prediction in social networks

Abstract

Trust prediction in Social Networks is required to solve the cold start problem, which consists of guessing a Trust value when the truster has no direct previous experience on the trustee. Trust prediction can be achieved by the application of machine learning approaches applied to reputation features, which are extracted from the available Trust information provided by witness users. Conventional machine learning methods work on a fixed dimension space, so that variable size reputation information must be reduced to a fixed size vector. We propose and give validation results on two approaches (1) a naïve selection of reputation features, and (2) a probabilistic model of these features. We report experimental results on trust prediction over publicly available Epinions and Wikipedia adminship voting databases achieving encouraging results.