Quality of Service (QoS) of Web services plays an essential role in selecting Web services by consumers. The dynamic QoS attributes of Web services have different values for different users. Therefore, the value of many Web services' QoS features for many users are undetermined, and these values should be predicted. The collaborative filtering (CF) method is one of the most successful approaches to predict these values. CF-based methods use the QoS values contributed by the other users for prediction and, consequently, the values contributed by unreliable users can decrease the accuracy of prediction. To utilize the reputation of users can be regarded as one of the conventional approaches to overcome this problem. In this paper, we have defined a concept called regional reputation that represents the reputation of a user for users in each geographical region. Regional reputation has been achieved with the combination of the location information of the users and their reputation. Subsequently, by combining this concept with the matrix factorization, we have proposed a prediction method called regional reputation-based matrix factorization. This approach has been able to improve the accuracy of prediction and be more persistent to the data contributed by unreliable users.

中文翻译：

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基于区域信誉矩阵分解的 Web 服务质量预测

Web 服务的服务质量 (QoS) 在消费者选择 Web 服务方面起着至关重要的作用。Web 服务的动态 QoS 属性对于不同的用户具有不同的值。因此，许多 Web 服务的 QoS 特性对许多用户的价值是不确定的，这些价值应该被预测。协同过滤 (CF) 方法是预测这些值的最成功的方法之一。基于CF的方法使用其他用户贡献的QoS值进行预测，因此，不可靠用户贡献的值会降低预测的准确性。利用用户的声誉可以看作是克服这个问题的常规方法之一。在这篇论文中，我们定义了一个称为区域声誉的概念，它代表了用户在每个地理区域中的用户声誉。通过结合用户的位置信息和他们的声誉来实现区域声誉。随后，通过将此概念与矩阵分解相结合，我们提出了一种称为基于区域声誉的矩阵分解的预测方法。这种方法已经能够提高预测的准确性，并对不可靠用户贡献的数据更加持久。