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Providing recommendations in social networks by integrating local and global reputation

机译：通过整合本地和全球声誉在社交网络中提供建议

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摘要

An important issue in Online Social Networks consists of the capability to generate useful recommendations for users, as peers to contact in order to establish friendships and collaborations, interesting resources to use and so on. This implies the necessity of evaluating the trustworthiness a user should assign to other members of his/her online community. In the past literature, a common approach for predicting trust is represented by a number of models that rely on "global" reputation: they are based on the evaluation of the behaviors of the users, that is shared across the entire community. These models, however, show an evident limitation due to the difficulty of taking the effects of malicious or fraudulent behaviors into account, thus making the feedback themselves. Other approaches, that consider also a local perspective of the trust, are limited by the fact they are supervised, i.e. they need a training phase in generating recommendations. In this paper, we propose a novel approach to extend global reputation models with a local reputation, computed on the ego-network of the user, by means of an unsupervised approach.
机译：在线社交网络中的一个重要问题在于能够为用户（与之建立联系以建立友谊和协作的用户）生成有用的建议的能力，以及使用有趣的资源等。这意味着需要评估用户应分配给其在线社区其他成员的可信度。在过去的文献中，一种预测信任的常用方法由许多依赖“全球”声誉的模型来代表：它们基于对用户行为的评估，并在整个社区中共享。但是，由于很难考虑恶意或欺诈行为的影响，因此这些模型本身表现出明显的局限性，因此无法自行做出反馈。其他方法也考虑了信任的本地视角，但受到监督的事实的限制，即它们在生成建议时需要培训阶段。在本文中，我们提出了一种新颖的方法，可以通过无监督的方法在用户的自我网络上扩展具有局部声誉的全局声誉模型。

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