结合网络结构和非线性恢复来提高声誉评估的性能

表征评价者的声誉对于消费者从在线评级系统中获得有用的信息尤为重要。此外，克服垃圾邮件对评级系统的攻击困难，获得可靠的评价者声誉是一个重要的研究课题。我们注意到，现有的评价者声誉评价方法大多只依靠评价者的评价信息和异常行为来建立声誉系统，而忽略了评价系统的系统性问题，包括评价者-对象的二方网络结构和非线性效应的影响。本研究我们提出了一种改进的声誉评价方法，通过将评价者-对象双方网络的结构与评级信息相结合，并引入惩罚和奖励因素。这种新方法在一个大规模的人工数据集和两个真实数据集上进行了实证分析。结果表明，所提出的方法在垃圾邮件攻击的情况下更加准确和稳健。这个新的想法为在稀疏的双比特评价网络中建立声誉评价模型提供了一种新的方法。

原文题目：Improving the performance of reputation evaluating by combining the structure of network and nonlinear recovery

原文：Characterizing the reputation of an evaluator is particularly significant for consumer to obtain useful information from online rating systems. Furthermore, to overcome the difficulties with spam attacks on the rating system and to get the reliable on reputation of evaluators is an important topic in the research. We have noticed that most of the existing evaluator reputation evaluation methods only rely on the evaluator's rating information and abnormal behavior to establish a reputation system, which miss the systematic aspects of the rating systems including the structure of the evaluator-object bipartite network and the effects of nonlinear effects. This study we propose an improved reputation evaluation method by combining the structure of the evaluator-object bipartite network with rating information and introducing penalty and reward factors. This novel method has been empirically analyzed on a large-scale artificial data set and two real data sets. The results show that the proposed method is more accurate and robust in the presence of spam attacks. This fresh idea contributes a new way for building reputation evaluation models in sparse bipartite rating network.