Rank learning algorithm for user reputation

Abstract

User reputation systems are widely used in Ecommerce website and social networks. In present most of the user reputation systems use the rule-based method or the voting systems to calculate user reputations. These systems heavily depend on the experience of experts. In this paper we try to use machine learning method to automatically learn user reputation in social networks. The social network we selected is a financial forum. A social network is seen as a directed graph, every user in the networks is a node in the graph, and the interactions between the users are the directed edges. Then we extract features of users from the social network graph. We translate the reputation learning problem into the document ranking problem, and use the listwise based rank learning method to build the reputation model. The reputation prediction model is represented as a linear model. We use the model to predict user reputation. The experimental results show that using rank learning method to predict user reputation is effective.