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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Rank learning algorithm for user reputation
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Rank learning algorithm for user reputation

机译：用户信誉等级学习算法

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摘要

User reputation systems are widely used in E-commerce 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.
机译：用户信誉系统广泛用于电子商务网站和社交网络。当前，大多数用户信誉系统使用基于规则的方法或投票系统来计算用户信誉。这些系统在很大程度上取决于专家的经验。在本文中，我们尝试使用机器学习方法来自动学习社交网络中的用户声誉。我们选择的社交网络是一个金融论坛。社交网络被视为有向图，网络中的每个用户都是图中的一个节点，用户之间的交互是有向边。然后，我们从社交网络图中提取用户的特征。我们将声誉学习问题转化为文档排名问题，并使用基于列表的排名学习方法构建声誉模型。信誉预测模型表示为线性模型。我们使用该模型来预测用户声誉。实验结果表明，使用等级学习方法预测用户声誉是有效的。

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